{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Cancer Type Specific EMOGI Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.path.abspath('../EMOGI'))\n",
    "import gcnIO, postprocessing\n",
    "import matplotlib_venn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "plt.rc('font', family='Arial')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_type = 'BRCA'\n",
    "\n",
    "pancan_model_path = '../data/GCN/training/Rev1_CNA_separated_all_networks/CPDB/'\n",
    "avg_model_path = '../data/GCN/training/BRCA_model_averaged/'\n",
    "patient_model_path = '../data/GCN/training/BRCA_model_patientwise/'\n",
    "#avg_model_path = '../data/GCN/training/2020_10_20_17_30_18/' # Thyroid\n",
    "#patient_model_path = '../data/GCN/training/2020_10_20_17_54_23/' # Thyroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncg_cancer_gene_path = '../data/pancancer/NCG/cancergenes_list.txt'\n",
    "oncokb_cancer_gene_path = '/project/gcn/diseasegcn/data/pancancer/oncoKB/cancerGeneList.txt'\n",
    "baileyetal_cancer_gene_path = '../data/pancancer/comprehensive_characterization_of_cancer_driver_genes_and_mutations/comprehensive_characterization_cancer_genes.csv'\n",
    "driverdb_cancer_gene_path = '../data/pancancer/driverdb/'\n",
    "ongene_oncogenes_path = '../data/pancancer/ongene_tsgene/Human_Oncogenes.txt'\n",
    "tsgene_tsgs_path = '../data/pancancer/ongene_tsgene/Human_TSGs.txt'\n",
    "digsee_expression_path = '/project/gcn/diseasegcn/data/pancancer/digSEE/expression/'\n",
    "digsee_mutation_path = '/project/gcn/diseasegcn/data/pancancer/digSEE/mutation/'\n",
    "digsee_methylation_path = '/project/gcn/diseasegcn/data/pancancer/digSEE/methylation/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cancer_genes_for_net(model_dir):\n",
    "    if model_dir.endswith('.h5'):\n",
    "        data = gcnIO.load_hdf_data(model_dir)\n",
    "    else:\n",
    "        data = postprocessing.get_training_data(model_dir)\n",
    "    network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "    nodes = pd.DataFrame(node_names, columns=['ID', 'Name'])\n",
    "    nodes['label'] = np.logical_or(np.logical_or(y_train, y_test), y_val)\n",
    "    # to exclude them from other databases\n",
    "    known_drivers = node_names[np.logical_or(y_train.reshape(-1), y_test.reshape(-1)), 1]\n",
    "    # get the NCG cancer genes\n",
    "    known_cancer_genes = []\n",
    "    candidate_cancer_genes = []\n",
    "    n = 0\n",
    "    with open(ncg_cancer_gene_path, 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            n += 1\n",
    "            if n == 1:\n",
    "                continue\n",
    "            l = line.strip().split('\\t')\n",
    "            if len(l) == 2:\n",
    "                known_cancer_genes.append(l[0])\n",
    "                candidate_cancer_genes.append(l[1])\n",
    "            else:\n",
    "                candidate_cancer_genes.append(l[0])\n",
    "    known_cancer_genes_innet = nodes[nodes.Name.isin(known_cancer_genes)].Name\n",
    "    candidate_cancer_genes_innet = nodes[nodes.Name.isin(candidate_cancer_genes)].Name\n",
    "\n",
    "    oncokb_genes = pd.read_csv(oncokb_cancer_gene_path, sep='\\t')\n",
    "    # remove low confidence genes\n",
    "    oncokb_no_ncg_highconf = oncokb_genes#[oncokb_genes['OncoKB Annotated'] == 'Yes']\n",
    "    oncokb_no_ncg_highconf = oncokb_no_ncg_highconf[oncokb_no_ncg_highconf['# of occurrence within resources (Column D-J)'] >= 3]\n",
    "    # remove all NCG genes\n",
    "    oncokb_no_ncg = oncokb_no_ncg_highconf[~oncokb_no_ncg_highconf['Hugo Symbol'].isin(known_cancer_genes_innet)]\n",
    "    candidate_cancer_genes_innet = candidate_cancer_genes_innet[~candidate_cancer_genes_innet.isin(oncokb_no_ncg)]\n",
    "    #oncokb_no_ncg = oncokb_no_ncg[~oncokb_no_ncg['Hugo Symbol'].isin(candidate_cancer_genes_innet)]\n",
    "    # remove genes that are not in the network\n",
    "    oncokb_innet = nodes[nodes.Name.isin(oncokb_no_ncg['Hugo Symbol'])].Name\n",
    "    \n",
    "    # comprehensive characterization paper genes\n",
    "    cancer_genes_paper = pd.read_csv(baileyetal_cancer_gene_path, sep='\\t', header=3)\n",
    "    cancer_genes_paper = pd.Series(cancer_genes_paper.Gene.unique())\n",
    "    cancer_genes_paper_notrain = cancer_genes_paper[~cancer_genes_paper.isin(known_drivers)]\n",
    "    cancer_genes_paper_innet = nodes[nodes.Name.isin(cancer_genes_paper_notrain)].Name\n",
    "    \n",
    "    # driverDB\n",
    "    driverdb_genes = []\n",
    "    thresholds_study = {'mutation': 25, 'CNV': 5, 'methylation': 2}\n",
    "    for evidence in ['mutation', 'CNV', 'methylation']:\n",
    "        driverdb_data = pd.read_csv(os.path.join(driverdb_cancer_gene_path, '{}_download_tab.txt'.format(evidence)), sep='\\t')\n",
    "        all_driver_genes = []\n",
    "        for index, row in driverdb_data.iterrows():\n",
    "            all_driver_genes += [i.strip() for i in row.driver_gene.split(',') if not i.strip() == '']\n",
    "        study_counts = pd.Series(all_driver_genes).value_counts()\n",
    "        driverdb_genes += list(study_counts[study_counts > thresholds_study[evidence]].index)\n",
    "    driverdb_genes = pd.Series(list(set(driverdb_genes)))\n",
    "    driverdb_genes_notrain = driverdb_genes[~driverdb_genes.isin(known_drivers)]\n",
    "    driverdb_innet = nodes[nodes.Name.isin(driverdb_genes_notrain)].Name\n",
    "\n",
    "    # OnGene + TSGene\n",
    "    oncogenes = pd.read_csv(ongene_oncogenes_path, sep='\\t')\n",
    "    tsgs = pd.read_csv(tsgene_tsgs_path, sep='\\t')\n",
    "    cancer_genes = list(set(list(oncogenes.OncogeneName) + list(tsgs.GeneSymbol)))\n",
    "    oncotsgs_innet = nodes[nodes.Name.isin(cancer_genes)].Name\n",
    "    oncotsgs_innet = oncotsgs_innet[~oncotsgs_innet.isin(known_drivers)]\n",
    "    oncogenes_innet = oncotsgs_innet[oncotsgs_innet.isin(oncogenes.OncogeneName)]\n",
    "    tsgs_innet = oncotsgs_innet[oncotsgs_innet.isin(tsgs.GeneSymbol)]\n",
    "\n",
    "    #return oncokb_innet, oncogenes_innet, known_cancer_genes_innet\n",
    "    return cancer_genes_paper_innet, candidate_cancer_genes_innet, known_cancer_genes_innet\n",
    "\n",
    "def compute_metric(y_true, y_pred, metric='aupr', cutoff=0.5):\n",
    "    if metric == 'recall':\n",
    "        return recall_score(y_true=y_true, y_pred=y_pred >= cutoff)\n",
    "    elif metric == 'aupr':\n",
    "        return average_precision_score(y_true=y_true, y_score=y_pred)\n",
    "    elif metric == 'auroc':\n",
    "        return roc_auc_score(y_true=y_true, y_score=y_pred)\n",
    "    elif metric == 'precision':\n",
    "        return precision_score(y_true=y_true, y_pred=y_pred >= cutoff)\n",
    "    else:\n",
    "        print (\"Metric {} unknown!\".format(metric))\n",
    "        return None\n",
    "\n",
    "def get_cutoff(model_dir, predictions):\n",
    "    data = postprocessing.get_training_data(model_dir)\n",
    "    _, _, _, _, y_test, _, _, test_mask, node_names, _ = data\n",
    "    cutoff = postprocessing.get_optimal_cutoff(predictions, node_names,\n",
    "                                               test_mask, y_test, method='IS',\n",
    "                                               colname='Prob_pos')\n",
    "    return cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Compute Thresholds for Both Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_avg_model = postprocessing.load_predictions(avg_model_path)\n",
    "pred_pancan_model = postprocessing.load_predictions(pancan_model_path)\n",
    "pred_patient_model = postprocessing.load_predictions(patient_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "thr_pancan = get_cutoff(pancan_model_path, pred_pancan_model)\n",
    "thr_avg = get_cutoff(avg_model_path, pred_avg_model)\n",
    "thr_pat = get_cutoff(patient_model_path, pred_patient_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRCA Model Threshold: 0.639\n",
      "BRCA Patient Model Threshold: 0.475\n",
      "Pancancer Model Threshold: 0.809\n"
     ]
    }
   ],
   "source": [
    "print (\"{0} Model Threshold: {1:.3f}\".format(cancer_type, thr_avg))\n",
    "print (\"{0} Patient Model Threshold: {1:.3f}\".format(cancer_type, thr_pat))\n",
    "print (\"Pancancer Model Threshold: {0:.3f}\".format(thr_pancan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Compare the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRCA Model Predicts 2962 Cancer Genes\n",
      "Pancancer Model Predicts 3704 Cancer Genes\n",
      "BRCA Patient Model Predicts 3232 Cancer Genes\n"
     ]
    },
    {
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       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000100629</th>\n",
       "      <td>CEP128</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  label  Num_Pos  Prob_pos  Std_Pred  \\\n",
       "ID                                                             \n",
       "ENSG00000131018    SYNE1  False       10     1.000     0.000   \n",
       "ENSG00000143631      FLG  False       10     1.000     0.000   \n",
       "ENSG00000198400    NTRK1   True       10     1.000     0.000   \n",
       "ENSG00000181555    SETD2   True       10     1.000     0.000   \n",
       "ENSG00000181143    MUC16   True       10     1.000     0.000   \n",
       "ENSG00000055609    KMT2C   True       10     1.000     0.000   \n",
       "ENSG00000128731    HERC2  False       10     1.000     0.000   \n",
       "ENSG00000083857     FAT1   True       10     1.000     0.000   \n",
       "ENSG00000288121     MDN1  False       10     1.000     0.000   \n",
       "ENSG00000158092     NCK1  False       10     1.000     0.000   \n",
       "ENSG00000143341    HMCN1  False       10     1.000     0.000   \n",
       "ENSG00000181449     SOX2   True       10     1.000     0.000   \n",
       "ENSG00000153707    PTPRD   True       10     1.000     0.000   \n",
       "ENSG00000171316     CHD7  False       10     1.000     0.000   \n",
       "ENSG00000204531   POU5F1   True       10     1.000     0.000   \n",
       "ENSG00000183117    CSMD1  False       10     1.000     0.000   \n",
       "ENSG00000206279     DAXX   True       10     1.000     0.000   \n",
       "ENSG00000186472     PCLO  False       10     1.000     0.000   \n",
       "ENSG00000178104  PDE4DIP  False       10     1.000     0.000   \n",
       "ENSG00000100629   CEP128  False       10     1.000     0.000   \n",
       "\n",
       "                 NCG_Known_Cancer_Gene  NCG_Candidate_Cancer_Gene  \\\n",
       "ID                                                                  \n",
       "ENSG00000131018                  False                       True   \n",
       "ENSG00000143631                  False                       True   \n",
       "ENSG00000198400                   True                      False   \n",
       "ENSG00000181555                   True                      False   \n",
       "ENSG00000181143                   True                      False   \n",
       "ENSG00000055609                   True                      False   \n",
       "ENSG00000128731                  False                       True   \n",
       "ENSG00000083857                   True                      False   \n",
       "ENSG00000288121                  False                      False   \n",
       "ENSG00000158092                  False                      False   \n",
       "ENSG00000143341                  False                       True   \n",
       "ENSG00000181449                   True                      False   \n",
       "ENSG00000153707                   True                      False   \n",
       "ENSG00000171316                  False                       True   \n",
       "ENSG00000204531                   True                      False   \n",
       "ENSG00000183117                  False                       True   \n",
       "ENSG00000206279                   True                      False   \n",
       "ENSG00000186472                  False                       True   \n",
       "ENSG00000178104                  False                      False   \n",
       "ENSG00000100629                  False                      False   \n",
       "\n",
       "                 OncoKB_Cancer_Gene  Bailey_et_al_Cancer_Gene  ONGene_Oncogene  \n",
       "ID                                                                              \n",
       "ENSG00000131018               False                     False            False  \n",
       "ENSG00000143631               False                     False            False  \n",
       "ENSG00000198400                True                     False             True  \n",
       "ENSG00000181555                True                      True            False  \n",
       "ENSG00000181143               False                     False            False  \n",
       "ENSG00000055609                True                      True            False  \n",
       "ENSG00000128731               False                     False            False  \n",
       "ENSG00000083857                True                      True            False  \n",
       "ENSG00000288121               False                     False            False  \n",
       "ENSG00000158092               False                     False            False  \n",
       "ENSG00000143341               False                     False            False  \n",
       "ENSG00000181449                True                     False             True  \n",
       "ENSG00000153707                True                      True            False  \n",
       "ENSG00000171316               False                     False            False  \n",
       "ENSG00000204531               False                     False            False  \n",
       "ENSG00000183117               False                     False            False  \n",
       "ENSG00000206279                True                     False             True  \n",
       "ENSG00000186472               False                     False            False  \n",
       "ENSG00000178104               False                     False            False  \n",
       "ENSG00000100629               False                     False            False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_avg = pred_avg_model[pred_avg_model.Prob_pos >= thr_avg]\n",
    "print (\"{} Model Predicts {} Cancer Genes\".format(cancer_type, preds_avg.shape[0]))\n",
    "preds_pancan = pred_pancan_model[pred_pancan_model.Prob_pos >= thr_pancan]\n",
    "print (\"Pancancer Model Predicts {} Cancer Genes\".format(preds_pancan.shape[0]))\n",
    "preds_pat = pred_patient_model[pred_patient_model.Prob_pos >= thr_pat]\n",
    "print (\"{} Patient Model Predicts {} Cancer Genes\".format(cancer_type, preds_pat.shape[0]))\n",
    "missed_by_avg_not_pancan = preds_pancan[~preds_pancan.index.isin(preds_avg.index)]\n",
    "missed_by_avg_not_pancan.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 8))\n",
    "v = matplotlib_venn.venn3([set(preds_pancan.index), set(preds_avg.index), set(preds_pat.index)],\n",
    "                      set_labels=['Predictions Pancancer', 'Predictions {}'.format(cancer_type),\n",
    "                                  'Predictions {} Patients'.format(cancer_type)])\n",
    "if not v.get_patch_by_id('10') is None:\n",
    "    v.get_patch_by_id('10').set_color('#3d3e3d')\n",
    "    v.get_label_by_id('10').set_fontsize(20)\n",
    "if not v.get_patch_by_id('11') is None:\n",
    "    v.get_patch_by_id('11').set_color('#37652d')\n",
    "    v.get_label_by_id('11').set_fontsize(20)\n",
    "v.get_patch_by_id('011').set_color('#4d2600')\n",
    "v.get_label_by_id('A').set_fontsize(20)\n",
    "v.get_label_by_id('B').set_fontsize(20)\n",
    "v.get_label_by_id('C').set_fontsize(20)\n",
    "if not v.get_patch_by_id('01') is None:\n",
    "    v.get_patch_by_id('01').set_color('#ee7600')\n",
    "    v.get_label_by_id('01').set_fontsize(20)\n",
    "if not v.get_patch_by_id('111') is None and not v.get_patch_by_id('101') is None:\n",
    "    v.get_label_by_id('111').set_fontsize(20)\n",
    "    v.get_label_by_id('101').set_fontsize(20)\n",
    "    v.get_patch_by_id('111').set_color('#890707')\n",
    "    v.get_patch_by_id('101').set_color('#6E80B7')\n",
    "if not v.get_patch_by_id('011') is None:\n",
    "    v.get_label_by_id('011').set_fontsize(20)\n",
    "if not v.get_patch_by_id('001') is None:\n",
    "    v.get_patch_by_id('001').set_color('#031F6F')\n",
    "    v.get_label_by_id('001').set_fontsize(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Most Mutated BRCA Genes According to TCGA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highly Mutated {} Genes Missed by EMOGI BRCA Model\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>label</th>\n",
       "      <th>Num_Pos</th>\n",
       "      <th>Prob_pos</th>\n",
       "      <th>Std_Pred</th>\n",
       "      <th>NCG_Known_Cancer_Gene</th>\n",
       "      <th>NCG_Candidate_Cancer_Gene</th>\n",
       "      <th>OncoKB_Cancer_Gene</th>\n",
       "      <th>Bailey_et_al_Cancer_Gene</th>\n",
       "      <th>ONGene_Oncogene</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000055609</th>\n",
       "      <td>KMT2C</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000083857</th>\n",
       "      <td>FAT1</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000173821</th>\n",
       "      <td>RNF213</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000107485</th>\n",
       "      <td>GATA3</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.004</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  label  Num_Pos  Prob_pos  Std_Pred  \\\n",
       "ID                                                            \n",
       "ENSG00000055609   KMT2C   True       10     1.000     0.000   \n",
       "ENSG00000083857    FAT1   True       10     1.000     0.000   \n",
       "ENSG00000173821  RNF213   True       10     1.000     0.000   \n",
       "ENSG00000107485   GATA3   True       10     0.994     0.004   \n",
       "\n",
       "                 NCG_Known_Cancer_Gene  NCG_Candidate_Cancer_Gene  \\\n",
       "ID                                                                  \n",
       "ENSG00000055609                   True                      False   \n",
       "ENSG00000083857                   True                      False   \n",
       "ENSG00000173821                   True                      False   \n",
       "ENSG00000107485                   True                      False   \n",
       "\n",
       "                 OncoKB_Cancer_Gene  Bailey_et_al_Cancer_Gene  ONGene_Oncogene  \n",
       "ID                                                                              \n",
       "ENSG00000055609                True                      True            False  \n",
       "ENSG00000083857                True                      True            False  \n",
       "ENSG00000173821               False                     False            False  \n",
       "ENSG00000107485                True                      True            False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_mutated_brca_genes = ['TP53', 'PIK3CA', 'CDH1', 'GATA3', 'KMT2C', 'MAP3K1', 'PTEN', 'NCOR1',\n",
    "                           'NF1', 'RUNX1', 'ARID1A', 'PIK3R1', 'KMT2D', 'MAP2K4', 'RNF213', 'SPEN',\n",
    "                           'FAT1', 'AKAP9', 'MYH9', 'MED12'] #'CYP19A1' BRCA gene according to literature\n",
    "print (\"Highly Mutated {} Genes Missed by EMOGI BRCA Model\")\n",
    "missed_by_avg_not_pancan[missed_by_avg_not_pancan.Name.isin(most_mutated_brca_genes)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sensitivity Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364 BRCA training genes\n",
      "80 BRCA testing genes with overlaps removed\n",
      "180 BRCA negative testing genes with overlaps removed\n"
     ]
    }
   ],
   "source": [
    "comprehensive, ncg_cand, ncg_known = get_cancer_genes_for_net(avg_model_path)\n",
    "\n",
    "data = postprocessing.get_training_data(avg_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "avg_test_genes = pd.DataFrame(node_names[y_test.reshape(-1) == 1], columns=['ID', 'Name'])\n",
    "avg_train_genes = pd.DataFrame(node_names[np.logical_or(y_train.reshape(-1), y_val.reshape(-1)) == 1], columns=['ID', 'Name'])\n",
    "avg_test_neg = pd.DataFrame(node_names[np.logical_and(np.logical_not(y_test.reshape(-1)), test_mask)], columns=['ID', 'Name'])\n",
    "\n",
    "data = postprocessing.get_training_data(patient_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "pat_test_genes = pd.DataFrame(node_names[y_test.reshape(-1) == 1], columns=['ID', 'Name'])\n",
    "pat_train_genes = pd.DataFrame(node_names[np.logical_or(y_train.reshape(-1), y_val.reshape(-1)) == 1], columns=['ID', 'Name'])\n",
    "\n",
    "data = postprocessing.get_training_data(pancan_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "pancan_test_genes = pd.DataFrame(node_names[y_test.reshape(-1) == 1], columns=['ID', 'Name'])\n",
    "pancan_train_genes = pd.DataFrame(node_names[np.logical_or(y_train.reshape(-1), y_val.reshape(-1)) == 1], columns=['ID', 'Name'])\n",
    "pancan_train_neg = node_names[np.logical_and(np.logical_not(y_train.reshape(-1)), train_mask), 1]\n",
    "\n",
    "avg_train_genes_nooverlap = avg_train_genes#[~brca_train_genes.Name.isin(pancan_train_genes.Name)]\n",
    "avg_test_genes_nooverlap = avg_test_genes[~avg_test_genes.Name.isin(pancan_train_genes.Name)]\n",
    "avg_test_neg_nooverlap = avg_test_neg[~avg_test_neg.Name.isin(pancan_train_neg)]\n",
    "\n",
    "print (\"{} {} training genes\".format(avg_train_genes_nooverlap.shape[0], cancer_type))\n",
    "print (\"{} {} testing genes with overlaps removed\".format(avg_test_genes_nooverlap.shape[0], cancer_type))\n",
    "print (\"{} {} negative testing genes with overlaps removed\".format(avg_test_neg_nooverlap.shape[0], cancer_type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(252, 7)\n",
      "(204, 7)\n",
      "Sensitivity of Pancancer on DriverDB Genes: 0.31\n",
      "Sensitivity of BRCA Averaged on DriverDB Genes: 0.22\n",
      "Sensitivity of BRCA Patient on DriverDB Genes: 0.20\n"
     ]
    }
   ],
   "source": [
    "driverdb_genes = pd.read_csv('../data/pancancer/driverdb/{}_cancer_genes.txt'.format(cancer_type), sep='\\t').set_index('gene')\n",
    "driverdb_genes = driverdb_genes[driverdb_genes.index.isin(node_names[:, 1])]\n",
    "print (driverdb_genes.shape)\n",
    "driverdb_genes = driverdb_genes[~driverdb_genes.index.isin(avg_train_genes.Name)]\n",
    "driverdb_genes = driverdb_genes[~driverdb_genes.index.isin(pancan_train_genes.Name)]\n",
    "print (driverdb_genes.shape)\n",
    "labels = ['Pancancer', '{} Averaged'.format(cancer_type), '{} Patient'.format(cancer_type)]\n",
    "c = 0\n",
    "for p in [preds_pancan, preds_avg, preds_pat]:\n",
    "    sensitivity = p[p.Name.isin(driverdb_genes.index)].shape[0] / driverdb_genes.shape[0]\n",
    "    print (\"Sensitivity of {0} on DriverDB Genes: {1:.2f}\".format(labels[c], sensitivity))\n",
    "    c += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity of Pancancer on BRCA Training Genes: 0.46\n",
      "Sensitivity of BRCA Averaged on BRCA Training Genes: 0.39\n",
      "Sensitivity of BRCA Patient on BRCA Training Genes: 0.46\n"
     ]
    }
   ],
   "source": [
    "avglabels = pred_avg_model[pred_avg_model.label].Name\n",
    "\n",
    "labels = ['Pancancer', '{} Averaged'.format(cancer_type), '{} Patient'.format(cancer_type)]\n",
    "c = 0\n",
    "for p in [preds_pancan, preds_avg, preds_pat]:\n",
    "    sensitivity = p[p.Name.isin(avg_test_genes_nooverlap.Name)].shape[0] / avg_test_genes_nooverlap.shape[0]\n",
    "    print (\"Sensitivity of {0} on {1} Training Genes: {2:.2f}\".format(labels[c], cancer_type, sensitivity))\n",
    "    c += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity of Pancancer on BRCA Training Genes: 0.68\n",
      "Sensitivity of BRCA Averaged on BRCA Training Genes: 0.54\n",
      "Sensitivity of BRCA Patient on BRCA Training Genes: 0.68\n"
     ]
    }
   ],
   "source": [
    "c = 0\n",
    "for p in [preds_pancan, preds_avg, preds_pat]:\n",
    "    sensitivity = p[p.Name.isin(avg_train_genes_nooverlap.Name)].shape[0] / avg_train_genes_nooverlap.shape[0]\n",
    "    print (\"Sensitivity of {0} on {1} Training Genes: {2:.2f}\".format(labels[c], cancer_type, sensitivity))\n",
    "    c += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PR Curve on the Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n",
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zhhMnTiRax83NjUGDBhmWV0ZFmV8Upf/6xF929yL9mCtXrmwInAAsLS2ZOnUqK1euZMWKFWkKnmxsbHBxceHhw4epbuN1k62CJyGEEEKI9DJv3jyzqcrN0el0TJo0iWnTphneWOfOnZvatWvj5eXF+fPnUcr4pBRzswr6WSB9XX1bLi5JH0W5evVqhg0bxqVLlwBwcnKiatWqVKxYke3bt2do//oZmUOHDnHo0KEE6z158sQoeEruGUzBwXEJkeNntoO4/WcHDx4ESHDf1JYtW7h9+zYFCxYE/nuepDLhhYeHG9UvWLAg1tbW3Lhxg+jo6ESXAt6+fduQiAPigif9/rCEeHp6MmjQIFxdXbGwsDA8d3z6cn2wa47+mj7Ajd+Pm5sbV65cSXQ8yeHo6JiqYPJ19TJm2xNCCCGESFfTpk1j5MiRVKxYkU2bNhEYGMj9+/f5+++/03QOlJOTE4BJtjqIm33QH2Lq7+9P165diYyMZOnSpVy+fJlnz57h5+eX4JlIae1fp9MZggt9vVGjRqGUSvCjfPnyqRpHzpw5AcwGE/pZp8aNG/PRRx+ZfFStWhWdTscff/xhuMfV1RVra2vu37+faL937twxzK5AXPBWv359nj9/zoEDBxK996OPPiJnzpyGdPbz589P9LVRShmWS9rY2ODp6cm1a9fMtn3t2jU8PDwMr4s5xYsXBxKevYqJiTEbjKZUUFBQhhxE/KqSmSchhBDiNZBRyRdeFUuWLMHS0pK1a9cazdIopbhw4YLhv1NKH2wcPnyYhg0bGl374YcfGDNmDNu3b2fNmjXodDpmz55tsufo/Pnz6dJ/fAcPHqRevXqMHz+et99+G4hLpmHO6NGjsbe354svvsDGxibF48iXLx+ASRp3pRSLFy9G0zTmzZtH4cKFTe7dt28f9erVY968eYwaNcqQFrx69eocPHjQaEbqRdeuXePOnTvUrl3baJlfnz592LFjBxMnTqRBgwZmx3vu3Dm2bduGo6Oj2SWPyVG3bl0WLVpEQECA0RLPu3fvEhAQQNu2bRO9v3r16tjY2LB7925iY2ON9sJduHCB0NDQJNORJyUiIoLnz59TsWLFNLXzOpGZJyGEEEK89uzs7IiNjTXZ+zF+/HjDbEJqDkvt0KEDjo6O/O9//+PGjRuG8idPnvDbb7/h7OxMzZo1sbOzAzCZSdmxYwdLlixJdf/e3t7UqlWLLVu2GB0IrNPp+P7771FK0bRpU4oUKUL9+vXZtGmTSUr2RYsWMW7cODZv3pyqwAkwnHV09uxZo/K9e/dy7do16tWrZzZwgrggpHjx4ly/fp1t27YZyt955x10Oh39+vUzzKDpRUVFMXjwYADef/99o2s9e/akZs2abNu2jY8//tgkacOlS5fo2LEj0dHRfPvtt8lacmnOu+++C8Dw4cMNZ0Uppfjmm2+AuMyIiXF1daV79+7cvHmTyZMnG8qjo6MZMmQIEHfQbVro06FL8JR8MvMkhBBCiNder169OHToEHXq1KFbt27Y2Niwa9cujh07Ru7cuXnw4IHRgbrJlTNnTn7++Wf69u1L5cqV6dChA05OTqxatYrAwEBWr16Nra0t3bt3Z9q0aXzyySfs3r2bfPnycerUKbZs2YK7u3uq+wf47bffqF+/Pq1btzacVbRz506OHz/OwIEDqV69OgBz5syhXr16dO3alZYtW1KuXDkuXryIr68vOXPmZPbs2anqH6B+/fo4Ozuzb98+o3L9kr1evXolen+fPn0YMWIEPj4+hkQf/fr1Y/PmzaxZs4aiRYvSpk0bPDw8ePDgAZs3b+b27dv07NnTZN+bpmmsW7eOli1b8ttvv7F69Wpat26Nh4cHly5dYuPGjURFRTFgwABDAJYaTZo0oXv37ixfvpxatWrRqFEjDhw4wN69e+nSpYvJDKM+UceLCTt++OEHDh48yMiRI/Hz86NixYrs2LGDEydO0L179wQPHE4u/XlkTZs2TVM7r5Wk1m6+Sh9Vq1ZVL4OGC/qqcvPLGX1M9FuS/h1FhSs12sX4Y5xH+vcjhBAi05w7dy6rh5DlRo8erQA1b968ZN+j0+nUTz/9pMqUKaPs7e1Vvnz5VIMGDdTSpUvVmjVrFKBGjhyplFLq2rVrClDt27dPsO+///7bqHzbtm2qcePGytnZWdnZ2anatWurDRs2mNSpXbu2cnFxUTly5FCVK1dWEyZMUIGBgcrCwkLVrVvXUNfT01O5urqa9L9r1y4FqIEDBxqVBwQEqB49eih3d3dlbW2tSpYsqaZPn65iY2ON6t28eVP169dPFShQQNnY2ChPT0/Vu3dvdeXKFaN6DRo0UIB6+vRp4i/sC7p166bs7e3Vs2fPlFJKhYeHK1dXV2Vra5tkO7du3VIWFhbKxsZGPXz40FCu0+nUvHnzVOPGjVXevHmVjY2Nyp8/v2rSpIlatmxZom2Gh4erP/74QzVs2FAVLFhQWVtbK3d3d9W2bVu1ZcuWZD9XYqKiotS4ceOUt7e3srW1VcWLF1fjxo1TERERJnUBFffW3NijR4/U559/rgoWLKhsbW1VyZIl1ZQpU1RMTEyifSf0vfiiVq1aKTc3NxUeHp7iZ3tZJfd3JHBEmYknNJWK9bMvq2rVqqmE1vJmJ40Wvscj9Y9RWQ+v4Qxv0COBO1IpOgIm5jEus7SFUabnSAghhHg5nD9/PsGMZUJkpUOHDlGrVi2jM4tE1rl79y6FCxfmm2++Yfz48Vk9nEyT3N+RmqYdVUqZnOose56EEEIIIUSGq1mzJm+++SY+Pj5ZPRRBXCp/Ozs7Bg0alNVDealI8CSEEEIIITLF9OnTOX78OL6+vlk9lNdaUFAQ06dPZ/To0YaDg0XySPAkhBBCCCEyRYUKFRg1ahTffPONIQOdyHzff/89xYoV44svvsjqobx0JHgSQgghhBCZZtSoUZw+fdro7CWRuSZNmoS/v7/R2VEieeS7VgghhBBCCCGSQYInIYQQQgghhEgGCZ6EEEIIIYQQIhkkeBJCCCGEEEKIZJDgSQghhBBCCCGSQYInIYQQQgghhEgGCZ6EEEIIIYQQIhkkeBJCCCGEEEKIZJDgSQghhBBCCCGSQYInIYQQQrxSxowZg6ZpJh8uLi5UrlyZKVOmEB0dbXKfl5eX2fssLCxwc3OjcuXKjBs3jvDw8AT7PnbsGB999BGlSpXCwcEBFxcXateuzc8//0xMTEyi4/b39zf0efjw4VQ9e//+/dE0jbJly6bq/teRm5sbXl5eKbqnWbNmfPbZZ2avBQYGYmVlhaZprFixIsE25s+fj6ZpjBkzJtG+NE1LcHzPnz9nzpw5NGjQgHz58mFjY4Onpyd9+vTh7NmzyX2cRD158oQBAwbg5eWFg4MDVatWZfny5cm+PzIyEmtra7M/W5qmceHCBaP6e/bsoUmTJjg7O2Nvb0/NmjVZvXq1UZ2oqCiKFi3K7Nmz0+UZU8Iq03sUQgghhMgE7du3p1KlSgDExsYSHBzM3r17GTp0KAcPHuTvv/82e9/o0aONPo+NjeXGjRusXbuW0aNHc+jQITZs2ICmaYY6Op2OMWPGMGHCBGxsbGjZsiVt27YlKCiILVu2MGDAAFauXMmmTZuwt7c32+/ChQuxs7MjMjISHx8fqlevnqLnjYqKYvny5Tg4OHDu3DkOHDhA7dq1U9SGSNq8efPw9/dn6dKlZq8vXryY2NhYHBwc8PHxoVu3bhkyjnPnztGpUycuXrxImTJlaNOmDW5ubpw9e5aFCxeyZMkSVq5cSfv27VPdx/Pnz2natCknTpyga9euFC5cmL/++ou33nqLhw8fMmDAgCTbOHv2LDExMTRv3pyaNWuaXHd3dzf895YtW2jdujU2Njb06NEDBwcHVqxYQefOnfn555/55JNPALCxsWHSpEn069ePdu3aUbBgwVQ/Y4oppV6bj6pVq6qXQcMFfVW5+eWMPib6LUn/jqLClRrtYvwxziP9+xFCCJFpzp07l9VDyHKjR49WgJo3b57JNZ1Op9q0aaMAtWPHDqNrnp6eKu6tkXl37txRefLkUYDasmWL0bXx48crQNWsWVPdvn3b6FpERITq2bOnAlS3bt3Mth0ZGaly5cqlWrZsqapXr66cnZ1VaGhoMp84zqpVqxSgxowZowDVt2/fFN3/unJ1dVWenp7JqvvkyROVI0cO9e233yZYp3z58qp06dKqW7duysLCQl2/ft1svXnz5ilAjR49OtE+AZPxPXz4UOXNm1dZWlqqOXPmKJ1OZ3T94MGDytnZWdnY2Kjjx48n59HMmjhxogLUrFmzDGXPnj1TZcuWVQ4ODur+/ftJtqF/zrVr1yZZt1y5csrKykodPnzYUHbnzh3l7u6unJycjH4mdDqdqlSpkurcuXOKnim5vyOBI8pMPCHL9oQQQgjx2tA0jb59+wKwe/fuFN2bP39+PvroIwB27NhhKA8ICGDcuHF4eHiwadMmChQoYHSfra0t8+bNw9PTk5UrV3L+/HmTtjds2MDjx49p2rQpnTp1IiQkJEVLoyBu5srCwoIBAwZQqlQpVqxYQUhISIraEIn77bffCAkJoX///mavHz9+nNOnTxu+jjqdjrlz56b7OL788kvu3bvHuHHj6Nevn9EsKEDNmjX54YcfiIqKYvLkyanuZ/bs2eTJk4ePP/7YUObs7MyIESMICwtjyZIlSbZx6tQpACpUqJBovYiICM6cOUP58uV54403DOX58+endevWhIaGcubMGUO5pml8+umnrF69mosXL6b00VJNgichhBBCvFasrOJ2Ldja2qb43ty5cwNx+zj0Fi5cSHR0NAMGDMDNzc3sfdbW1syaNYs//vjDaJnSi20ANG/e3LDMy8fHJ9njevToEZs2baJq1arkypWL7t278/z5c5OlZdOnT0fTNH777TeTNu7evYulpSWtWrUylEVFRTFp0iTKlCmDnZ0duXPnpmfPnly9etXoXv0+sx07dlCjRg1sbW0pWbIkoaGhAOzfv59OnToZ9uXkyJGDpk2bsmvXLpNxXLlyhR49epAnTx6cnJxo1aoV58+fp1ixYjRs2NCobnLHp3+NPv30UwoUKICDgwNNmjTh9OnTyX6No6OjmTVrFk2aNCFv3rxm67z4dWzTpg0ODg7MmzcPnU6X7H6SEhISwsqVK3F2dmbgwIEJ1nv33XcZP348n376qaEsoX19L3706dMHiPs63Llzh3r16mFpaWnUdqNGjYDk/QHi1KlTuLi4JLmvzNbWFkdHR+7evWuyJ/HOnTsAeHh4GJV369YNKysrpk+fnuQ40ovseRJCCCHEa0Mpxfz587G0tKRDhw4pvn/z5s0AVKxY0VC2adMmIO4Nc2LatGljtvzx48ds3LiRcuXKUaZMGQBq1KjBwYMHOXfunKEsMUuXLiU6Opru3bsD8NZbbzF27Fh8fHz48MMPDfV69OjBkCFDWLFihWEWTW/FihXodDp69eoFxAULLVu2ZOfOnVSvXp0BAwbw4MEDVqxYwebNm9m9ezflypUzaqNnz56UKlWKzz77jJCQEJycnFi7di2dO3cmd+7cdOzYEWdnZ86cOcOmTZvw8/Pjn3/+MexNu3z5MrVr1+bx48d06NABb29v1q9fT926ddHpdEZ7W1IyvtDQUOrXr8/58+d58803KV++PH5+fjRo0IDw8PAEg94X7dmzhzt37vDVV1+ZvR4TE8PSpUvJmTMnTZs2xdramrZt27J8+XI2b95sFJSmhZ+fH+Hh4bRo0QJHR8cE69nZ2TFy5EijskGDBhEUFJRo+/qvxZUrVwAoWrSoSZ28efNiZ2dHQEBAkuM9ffo0hQoVYvjw4axcuZJbt27h7e3NRx99xOeff26YNdM0jY8++ojp06fzwQcfMHHiRJycnPjll1/Yvn077du3x9vb26htFxcXatasyYoVK/jll1+wsMj4eSEJnoQQQojXwRjXrB5Byo0JTtPta9as4fr160Bc0BQSEoKfnx9nzpxh1qxZyQpKIG524+bNm/zyyy/4+vpSrFgx3n77bcP127dvA1CiRIlUjXPZsmVERUXRo0cPQ9nbb7+Nv78/Pj4+yfqr+sKFC9E0zRA8lSpVisqVK/PPP/9w+vRpypcvD0C+fPlo3LgxO3fu5P79++TJk8doHI6OjoYEAzNnzmTnzp0MGTKE77//3lDv888/p3bt2rz33nsmWQELFSrEzp07jd7EDh06FFdXV44fP27U35QpUxg6dCgrVqwwvGEfPHgwDx8+ZOXKlXTp0gWACRMm0KRJE/bt22fUV0rGN3XqVM6fP8+YMWMMCUFiYmJ49913E0z8EJ+fnx8A1apVM3t9y5Yt3L9/n379+mFtbQ3EfR2XL1+Oj49PugVPafl+GzRoULLrPn78GCDBwNLFxYXg4MR/Ru/du8eDBw948OAB4eHhtGvXjufPn7NhwwYGDRrE8ePHmT9/vqH+1KlTcXJyYvz48YZZPIBevXoxZ84cs31Uq1aNvXv3cuLECapUqZLs50stWbYnhBBCiFfS2rVrGTt2LGPHjmXcuHHMmDGD48eP4+TkxNOnT4mNjTV7X/xlTLa2thQvXpzp06fToEEDtmzZYrTkT/+XfGdn51SNc9GiRUDcbJFe9+7dsbS0ZNGiRURFRSV6/4ULFzhy5Aj16tUzmpnRB3i///67Uf1evXoRGxvLqlWrDGXXr1/H39+fDh06GGYz5s6di5ubGxMnTjS6v1q1anTr1o1//vnHJB12x44djQInnU7HpEmTWLRokVHgBBiW4D148ACIW1a3ceNG6tWrZwicIG4514vBkV5Kxrd06VLc3NwYPny4oZ6VlRXTpk0z2S+UkGPHjgEkGHTrv44vBsEtWrQgZ86c+Pr6cv/+/WT1k5S0fr8ll37pXELLW21tbYmIiEi0jfv371O2bFm6dOnCuXPnmDFjBnPmzOHs2bNUq1aNBQsWsG7dOkP99evXM3v2bHLlysUHH3xA//79KVy4MMuWLWPmzJlm+9Cn5T969GgqnjLlZOZJCCGEEK+kefPmGfZvQFza5QsXLvDtt98yfPhwAgICmDdvnsl9+pkJnU7HyZMn8fX1JVeuXCxbtozGjRub1M+VKxeBgYE8ffrUZE9GUgICAvD396dGjRpGS5Ly5MnDm2++ydatW1mzZk2i6a71f6F/8U27/vOhQ4fy559/MnXqVMOb4E6dOtG/f3+WL19u2A+jT06hX7IXGhrKxYsXyZs3LxMmTDDp8969ewCcOHHC6EypIkWKGNWzsLCgY8eOANy4cYMzZ85w5coVzp07Z9jvpA9ijx49ik6nM5uivUaNGoa9aikdn7e3N5cuXaJBgwaGGSG9fPnyUaRIkQQD6Rc9ePAACwsLcubMaXLt2bNnrF27lnz58tGgQQNDuY2NDV26dGHOnDksWLCAIUOGGL02yfVi3Vy5cgHw9OnTZN+vN3PmzGQt2+vQoYMhpX5CwXtkZGSiywYhbnnri0ke9Nzc3JgyZQqNGzdm6dKltGvXjmvXrtG1a1cKFizIwYMHDcF2SEgILVu2ZPjw4VSsWNFkBk+/h/Dhw4eJjiW9SPAkhBBCiNeCo6MjVatW5e+//8bb25v58+czbNgwSpYsaVQv/qGl+j073bt3Z8+ePZQuXdroure3N4GBgVy+fDnR4Ck4OJiwsDDy5ctnKNMHPvoDcs1J7KwgpRR//vknEHdArrkscE+ePOGvv/4yzEQ5OTnRvn17li1bxp07dyhQoADLli0jd+7cNGnSxDBWiAtCxo4dm+AzPXnyxOhzc2dYnT59ms8//9yw7M3a2poyZcpQrVo1AgICiMsKHTfzBJhNxmBpaWlI1pHS8emDjIRmanLmzJmsN97BwcEJntG1YsUKIiIiCAwMNEmuoDd37lyj4MnFxQXA7IHNevoDmfV1AUOQffny5STHfPnyZby8vAyB58yZM7lx40ai9/Tu3ZsOHTqQI0cOgASX5j179sxkNjEl9Evsrl27BsCff/5JdHQ0o0aNMmrX2dmZ6dOnU6NGDebPn28SPOkDuNQEk6khwZMQQgghXis2NjbUrl2blStXcurUKZPgKb727dszcuRIxo4dS/v27Tl27BhOTk6G6y1atGD//v1s3bqVWrVqJdjOnDlzGDJkCCNHjmT8+PEopVi8eDEWFhZ88MEHZoOnP//8kx07dnDjxg08PT1Nru/atYubN29SqlQpoxkPvbt377J+/Xp8fHyM9mn16tWLpUuXsmrVKlq2bMmJEyf47LPPDG+y9c9Xr1499uzZk+jrk5iQkBCaNm1KcHAwP/zwA02bNqVUqVLY2Njg7+9vlOpaHyA8e/Yswbb0UjI+fQCSUBCgzwiYlJw5c3L58mViYmKMZsHgvyC4V69eZmdj1q1bR0BAAHv27KF+/frAfzMmiS3n02eZezFDY7169XB0dGTfvn2Eh4cnGNBFRUVRvXp1YmNjuX79Ojly5DDsAUwO/Z4qfXDzosDAQCIiIpL82bl9+zaXL1+mXLlyJlkm9V8XOzs7AG7dugVg8scJ+G9p3s2bN02u6WfSEnod0psET0IIIcTrII3JF141+r9Su7omL5HGyJEj8fX15ejRowwZMoTZs2cbrr399tuMHz+eWbNmMWjQILNthoWFGfYeNW3aFIhL83zjxg3efPNNs6nDIS6pwdy5c5k7dy7jxo0zua5/0z5ixAjDkrv4/ebNmxc/Pz+uXLliyJzWrFkzcufOzbp16wzBQ8+ePQ33ubq6UrhwYc6ePWv2DfrChQu5evUqffr0STQFtT4xxVdffcWXX35pdE1/3pV+5qlKlSpommaShALg3LlzRsFTSsdXunRpjh8/blI3KCiIy5cvm5zNZU6+fPlQSvH48WOjmZFr166xb98+ihYtatj3FF/BggUZNWoUPj4+huCpSpUq2NjYsG/fPnQ6ndllfPokGTVq1DCU2dra8tZbbzF37lymT5/OiBEjzPa5YMECnj59SqNGjQyzSClRuHBhChcubHZ8+lnExP5YAHF/MBg/fjzTpk3jiy++MPts+gQc+tc0ICCAmjVrGtW9dOkSYH5WUj9jWahQoeQ+WppIwgghhBBCvFb8/f3x8/MjR44c1KtXL1n3WFlZ4ePjg6WlJb/++isHDx40XPP29mbw4ME8evSIFi1aEBgYaHRvcHAwPXv25NKlS7Rt29bw5lkf+LwYtMSnP9DX3FlBYWFh/PXXXzg4OCSYdt3BwYHu3bujlDI6rNXKyoru3buzd+9elixZQrFixYzeoAP06dOHJ0+eMGzYMKO+z507x4ABA5g+fbrZ/T8v0s8qxJ9duXnzpmG5nX7ZWoECBWjatCnbtm1j48aNhrqRkZFGy91SM74+ffoQGhrKsGHDDMGaUopvvvmGmJiYRJ9BT5/2PH6SjEWLFqGUSvTr2Lt3bywsLFi1apVhBszBwYFOnTpx8eJFs4FxYGAgEyZMwNramnfeecfo2sSJE3F1dWXMmDH4+PgYnklv8+bNDBw4ECsrKyZNmpSs5zPnnXfe4fbt28yaNctQFhISwsSJE7G3tzcZV3xdunRB0zSmTZtmSAyif7bhw4djbW1tSKWvrztx4kRDQARxh+fqv/7x9/UBhj1VLx4fkKGUUq/NR9WqVdXLoOGCvqrc/HJGHxP9lqR/R1HhSo12Mf4Y55H+/QghhMg0586dy+ohZLnRo0crQLVv316NHj3a8DFy5EjVsWNHZW1trTRNUwsXLjS6z9PTU8W9NUrYF198oQBVrlw5FRUVZSiPiYlRffv2VYCytbVVnTp1UkOGDFE9e/ZUHh4eClB16tRRT58+VUopFRYWppydnZW9vb0KDg5OtM/ixYsrQG3YsMGofNGiRQpQb7/9dqL379+/XwEqf/78KiYmxlDu7++vAAWo0aNHm9wXFhamatSooQBVuXJlNXjwYNW3b1/l5OSkNE1TS5b8995E/5r//fffRm2EhoYqLy8vBaiWLVuqYcOGqZ49eyonJyfl7u6uNE1TzZo1M9S/cOGCcnNzU5aWlqpz585q4MCBqnTp0srd3V0BqnHjxqkaX2RkpKpTp44CVK1atdTgwYNVzZo1lZOTk8qdO7fy9PRM9DVUSqmDBw8qQI0dO9aovFixYgpQAQEBid7ftGlTBaiff/7ZUHb37l1VsmRJBagKFSqozz//XH3zzTfqrbfeUk5OTsrCwkLNnj3bbHv+/v4qV65cClBlypRR/fv3VwMHDlT169dXgLKxsVGLFy9O8rkSExwcbPj+69Spk/r666+Vt7e3AtRPP/1kVPfatWtq9OjRat68eUblw4YNU4Dy8PBQn3zyiXr//fcNPxPxn23s2LGGuv3791efffaZ4fXt0aOH0ul0JmOsVKmSypkzp9H3dmKS+zsSOKLMxBNZHtBk5ocET/FI8CSEEK8cCZ7+eyMf/8Pa2loVKFBAderUSfn5+Zncl5zgKTQ01FBvwoQJJte3bNmiOnbsqIoWLars7OyUs7OzqlOnjvr111+N3twtWbJEAapbt25JPs93332nANWxY0ej8mbNmilAbdy4Mck29G/Q165da1ReokSJRN/4h4WFqbFjx6rSpUsrW1tblTt3btW8eXOT1y+h4EkppS5duqQ6deqkcufOrRwdHVXp0qXVxx9/rO7evasqVaqk7O3tVUhIiKH+2bNnVbt27ZSrq6tycHBQbdq0UadOnVKAatOmTarGp5RSz58/V8OGDVOFCxdWdnZ2qmbNmurAgQOqYsWKyQqeYmNjVd68eVWdOnUMZQcOHFCAql69epL367/mlStXNioPDg5WEydOVFWqVFE5c+ZUtra2ysvLS/Xo0UMdPHgw0Tbv37+vxo8fr6pWrao8PDyUtbW18vT0VO+//746f/58kmNKjnv37qn33ntPeXh4KAcHB1WlShW1dOlSk3q7du1SgGrQoIHJtUWLFqk33njD8DPRqFEjtXnzZrP9rVq1StWtW1c5OjoqOzs7ValSJTVr1iyzgVNgYKCytLRUH3/8cbKfJ63BkxZ37fVQrVo1deTIkaweRpIaLXyPR+ofo7IeXsMZ3sB0qjJNoiNgYrwsKZa2MOqB+fpCCCGyvfPnz5vdcC1EdqfT6bh69Sqenp4mKcWvXbuGt7c3/fv3N9pvltkmT57MN998Q0BAAMWLF8+ycYg4U6ZM4ZtvvuH8+fPJPjQ4ub8jNU07qpQyORFZ9jwJIYQQQogsp2kalStXpnz58iZnC02dOhWARo0aZcXQDD799FNy5MhhcvCwyHw6nY65c+fStWvXZAdO6UGy7YnX1/NHcPcE5C4FrgWTrC6EEEKIjKNpGh9//DE//PADFSpUoGXLllhaWrJ//34OHTpE8+bN6dKlS5aO0dnZme+++86QPTAt5xyJtFm8eDF37txh8+bNmdqvzDyJ18+Tq7B2AEwrCX92hhll4djCrB6VEEII8dr7/vvv8fHxwc3Njfnz5/Prr7/y/PlzJk+ezPr16xM8SDgzffTRR9SsWTPRw3lFxoqMjOTbb79l8uTJFClSJFP7lpkn8fp4eBH2ToPTK0EZp3tl6yio1BMszJ8KLoQQQoiMZ2Fhwfvvv8/777+f1UNJkKZpbN++PauH8VqztbVN0YG/6UmCJ/Hqu3cG9kyFc2uJS7hkRkQQhNwD16QPyRNCCCGEEK8nCZ7Eq+vOUdjzA1zcmHRdIYQQQgghkiDBk3j1PLoEW0bApS1ZPRIhhBBCCPEKkeBJvDoigmH3FPD/FXQxWT0aIYQQQgjxiskW2fY0TeunadolTdPCNU07qGlarSTq99A07YymaRGapl3QNO2jzBqryIZ0Oji2CH6qCgdnJR44OeWF5pPA0SPzxieEEEIIIV4JWR48aZrWG/gVWAx0BoKALZqmmc07qGna28AS4CzQHvgZmKpp2jeZMmCRvdz6B3zehHUD4PnDhOu5FoLW02DgSaj1CVhYJ1xXCCGEEEIIM7J02Z4Wl6x/LDBHKTX237JtwEVgMPC5mdu+AQ4CbymlFHGBVhQwQ9O0OUqpx5kzepGlngXC9jFwalni9VwLQYOhUKE7WNlkytCEEEIIIcSrKav3PBUDPIF1+gKlVLSmaRuAFgncUwKY9G/gpLcPsAcaAKszaKwiO9DFwqFfwG8SRIUmXM/KHuoOhjqfg7V95o1PCCGEEEK8srI6eCrx77+X45VfBYpqmmaplIqNd+0WUDhemX6Jn1f6Dk9kK8F34O+P4PrexOuV7QhNx4NbocwZlxBCCCGEeC1k9Z4nl3//DYlXHkLc2BzN3LMYeEfTtPc1TXPTNO0NYBJxp5+aqy9eBefWwi+1Ew+c8pSDPhug63wJnIQQ4jU2ZswYNE0z+XBxcaFy5cpMmTKF6Ohok/u8vLzM3mdhYYGbmxuVK1dm3LhxhIeHJ9j3sWPH+OijjyhVqhQODg64uLhQu3Ztfv75Z2JiEs8E6+/vb+jz8OHDyX5ePz8/s+O2tLTE2dmZypUrM2HChETHnRxLly7l6tWrhs/nz5+PpmnMnDkzTe0m1z///MPWrVtTdE+/fv1o27at2WuRkZHkyJEDTdOYMmVKgm3oX98+ffok2pf++8ec6OholixZQvPmzSlUqBA2Njbkz5+fbt26ceDAgWQ/T2LCwsIYNWoUxYoVw97enjJlyvDzzz9jvFgrcYUKFTL7vaRpGps3bzaqe+LECdq1a4ebmxtubm40btyYbdu2mbT5zjvv0K1btzQ/X3aR1TNP+u+whL6qOjNl3wF5gd8BH+AJcXujFgFhJh1o2ofAhwCFC8efsBLZXmQobB4KxxcnXMc+BzQeCVX6gGVWf0sLIYTILtq3b0+lSpUAiI2NJTg4mL179zJ06FAOHjzI33//bfa+0aNHG30eGxvLjRs3WLt2LaNHj+bQoUNs2LDB6I2yTqdjzJgxTJgwARsbG1q2bEnbtm0JCgpiy5YtDBgwgJUrV7Jp0ybs7c0vJ1+4cCF2dnZERkbi4+ND9erVU/S8FStWpEOHDkZjCg4OZuPGjYwaNYrt27ezY8cOLC0tU9QuwNChQ5kyZQrHjx83lFWqVInRo0dTs2bNFLeXUhs2bKBdu3ZMmzaNZs2aJeueXbt2MX/+fE6fPm32+rp16wgKCsLBwYG5c+cyZMiQ9Byywe3bt+natSuHDh3Cy8uLJk2akDt3bi5fvsyaNWtYtWoVP/30E59++mmq+4iNjaVr165s3LiRVq1a0aVLFzZt2sSAAQO4du0aP/zwQ5JtPHnyhNu3b1OjRg1atDDdPVOsWDHDf+/evZuWLVsSERFBu3bt8PLyYvPmzTRv3tzkWSZOnEjJkiVZt24d7dq1S/UzZhtKqSz7AFoTFzgVi1c+GIhJ4l5noAxgS9y+KQX0TeyeqlWrqpdBwwV9Vbn55Yw+JvotSf+OosKVGu1i/DHOI/37Sa3bR5T6XyXTMRo+XJXy/UKp549T3vYPpUzbC7qd7o8ghBCZ7dy5c1k9hCw3evRoBah58+aZXNPpdKpNmzYKUDt27DC65unpqeLeGpl3584dlSdPHgWoLVu2GF0bP368AlTNmjXV7dvG/z+JiIhQPXv2VIDq1q2b2bYjIyNVrly5VMuWLVX16tWVs7OzCg0NTdbz7tq1SwGqd+/eZq+HhYWpihUrKkAtWrQoWW3G17t3bwWo48ePp+r+tJo3b54C1IwZM5JVPzo6WhUrVky9++67CdZp06aNcnFxUV988YUC1O7du83WS+r11TP3/RMeHq7Kli2rADV27FgVFRVldP3ixYsqf/78StM0tWHDhmQ9mzl//vmnAtRXX31lKIuKilKNGzdWmqapU6dOJdmG/jn/97//JVovJiZGeXt7K0CtXLnSUB4WFqbq16+vrK2tVUBAgNE9AwcOVAUKFFDh4eEpfLL0l9zfkcARZSaeyOple5f+/dc7Xrk3EGDuBk3TGmua1lApFaKUOqeUigQq/Hv5RMYMU2QqXSzsnQZzm8GTq+brOOeDd9fGpR93yJm54xNCCPHS0jSNvn37AnF/PU+J/Pnz89FHcUdL7tixw1AeEBDAuHHj8PDwYNOmTRQoUMDoPltbW+bNm4enpycrV67k/PnzJm1v2LCBx48f07RpUzp16kRISAjLly9P6eOZZW9vz4ABAwDYuHFjurSZ3a1atYrLly/z+efmEjfDw4cP2bx5Mw0bNjQsKfPx8Un3cUyaNImzZ8/Sr18/vv32W6ytjY9KKVGiBPPmzUMpxbhx41Ldz88//4yVlRXDhw83lFlbWzNhwgSUUsydOzfJNk6dOgVAhQoVEq33zz//cPXqVZo3b06XLl0M5fb29kyaNIno6GhmzZpldM+AAQO4c+cOCxcuTMljZUvZIXi6BXTQF2iaZk3cjNSOBO55C/jxhfoa8AlwEziVUQMVmST4NixoBzvGJXzYbak20P8AeDfI3LEJIYR4JVhZxS3xtrW1TfG9uXPnBuL2y+gtXLiQ6OhoBgwYgJubm9n7rK2tmTVrFn/88Qfu7u4m1/VvKps3b54hb+b1Ad3jx8YnuixcuJCGDRuSI0cObGxsyJcvHz179jTa2+Tl5cWCBQsAqFy5Ml5eXkDCe54uX75Mr169yJMnD7a2tpQuXdrwpvpFDRs2xMvLi9u3b/P222+TK1cuHBwcqF+/Pn5+foZ6ffr0MQS8gwcPRtM0rl+/nujzTp8+nVKlSlG1alWz15csWUJMTAzNmzenRo0aeHl5sWrVKoKDgxNtNyWUUsybNw+AESNGJFivWbNmTJ48mTFjxhj2JzVs2DDBvUf6j4YNGwJx34uHDx+mUqVK5MiRw6jt6tWr4+DgkKw/FCQ3eLp27RqA2eWa+nv37dtnVF6sWDGqV6/O9OnTU7QHKzvK0g0iSimladpkYJamaU+B/cAAwB2YAaBpWlHAQyl16N/bfgfe0zRtJnEpznsCzYEeyjQzn3iZ3D4CS7pBWAJHdVk7QIvJUOVdSGBDphBCCJEYpRTz58/H0tLSaH9Qcuk3zVesWNFQtmnTJiAu8ElMmzZtzJY/fvyYjRs3Uq5cOcqUKQNAjRo1OHjwIOfOnTOUpcXly3GJjV+cFfvqq6+YNm0aFStWpE+fPmiaxu7du1myZAl79+7l4sWL2NvbM2jQIObPn8/JkycNyTAScuzYMRo3bkx4eDidOnXC09OTvXv3Mnz4cPbs2YOvr6/RnqvQ0FDq1auHg4MDvXv35t69eyxfvpzmzZtz7NgxypYtS4cOHQgKCmLt2rU0b96cmjVrJhikAly5coV//vmHgQMHJlhn4cKFWFpa0rlzZwC6d+/O999/z59//sknn3yS3Jc1UWfOnOHWrVuUKlUKT0/PROsOHTrU6PM+ffoYgqOE6IPYGzduEBMTQ9GiRU3qWFpaUqhQIQICzC7oMnLq1Cly5cqFj48PCxYs4OrVq+TLl4933nmH4cOHG/7YoP/3xT8g6OmDzxs3bphca968OePHj+f48eNUqVIlyfFkV1m+u14pNVvTNHtgIHF7nU4AzZVS+j95jAJ6829yCaXUP5qmdQXGE5cIIgDorpRamdljF+no4mZY2QdiEsgElK8SdJ4L7sXMXxdCCJGo8gvKZ/UQUux0b/Mb/ZNrzZo1hhkKpRQhISH4+flx5swZZs2aleygJCoqips3b/LLL7/g6+tLsWLFePvttw3Xb9++DcQtwUqNZcuWERUVRY8ePQxlb7/9Nv7+/vj4+DB9+vRUtav39OlTQxudOnUC4M6dO8yYMYP69euzc+dOo4CmdevWbNy4kb1799KsWTMGDRrEiRMnOHnyJB9//LEhCUd8Sil69+5NZGQkBw4cMJr1+eKLL5gxYwa//fabUXDy+PFj6taty8qVKw1L2sqVK8eIESNYtGgRkydPNgqeWrRowaBBgxJ9Xv2sVbVq1cxeP3v2LMeOHaNp06bkyZMHiHu9v//+e3x8fNIteErL90VSmf1epJ9NTCigdHV15eLFi8TExBhmXePT6XScPXuWsLAwZsyYQadOnWjUqBFbt25l3Lhx7N+/n82bN2NlZWX4uq5fv57x48cbtbluXdzRreZm8PRfDz8/Pwme0kopNQ2YlsC1PkCfeGV/A+ZT5IiXz9H54DsYlLnkihrUHQQNh4OVTSYPTAghxMts7dq1rF271qQ8R44cPH36lNjYWLOZ5xJKNw3QoEED/vjjD6Mlf0FBQQA4OzunapyLFi0C4K233jKUde/enS+++MIQQNjYJP3/wBMnTjBmzBjD5zqdjrt377J+/XoePHhAt27dDLNfdnZ2LFq0iDJlypi8Bg0aNGDjxo08ePAgRc/h7+/PmTNn+PTTT02Wy40fP56ff/6ZefPmmQQnX375pdFeoFatWjFixIgkl+Yl5NixYwAJBsf61/vFYLVChQqULVuW48ePc+zYsXR5c5/W74vk0i+HTGgZqr48IiICJycns3UePnxI8eLFyZEjB3///bchEIuIiKBr1674+voye/ZsPv/8czw9PenSpQurVq2ie/fuTJo0iTx58rBhwwa++eYbHBwczM5KlS1bFoCjR4+m9ZGzVLYInsRrSinwmwy7J5u/7pwfOv0GRepn7riEEEK8EubNm2f0F/znz59z4cIFvv32W4YPH05AQIBhT8qL9KnKdTodJ0+exNfXl1y5crFs2TIaN25sUj9XrlwEBgby9OlTPDw8UjTGgIAA/P39qVGjBt7e/+XPypMnD2+++SZbt25lzZo1yTon5+TJk5w8edLwuaWlJa6urpQrV46xY8fy4YcfGo357bffRqfTcebMGc6fP8/Vq1c5efIk27dvB+LSX6eE/k3xlStXjII4PWdnZ06ePIlSyihAjT8z4+rqCphfFpYc+qDP3N4ynU7Hn3/+ia2trWEWTq9nz54MHz4cHx8fZs+ebSi3sEh+ioAXnytXrlxA3MxfSs2fPz/J4NHLy4s+ffoYUt9HRUWZrRcZGYmmaTg4OCTYVp48eThx4oRJuZ2dHT/++CO+vr4sXbrUkIDj999/59GjR6xevZrVq1cDcfv6pk2bxpw5c8wu29N/PR4+fJjoc2V3EjyJrBEbA76D4Pgi89fzVoCeq8A5T6YOSwghxKvL0dGRqlWr8vfff+Pt7c38+fMZNmwYJUuWNKoX/43/2rVr6dy5M927d2fPnj2ULl3a6Lq3tzeBgYFcvnw50eApODiYsLAw8uXLZyjTJ4rQH5Brjo+PT7KCp969ezN//vwk6+mtXr2aYcOGcelSXPJjJycnqlatSsWKFdm+fXuKN/brZ1o2b95scqDqi0JDQ41mY+LPmOhfh9QmFtAvGTMXLOzcudOwnC6hZW5Llixh2rRphqDExcUFwOzByi8KDw831AUMwbB+v1lirl27Rr58+bCzswPigqekkjw0aNCAPn36GJJEJJTsIjg4GCcnpxQFgS8qUqQIOXLkMCSKgLjXbufOnWzfvp1jx47h4uJC69atKVy4MOPGjTMsh3yRo6MjkLpgMjuR4ElkvqjnsLIvXNpi/rp3I+i+CGwzdppbCCHE68nGxobatWuzcuVKTp06ZRI8xde+fXtGjhzJ2LFjad++PceOHTNa/tSiRQv279/P1q1bqVWrVoLtzJkzhyFDhjBy5EjGjx+PUorFixdjYWHBBx98YDZ4+vPPP9mxYwc3btxIMulASvj7+9O1a1cKFizI0qVLeeONN/D29kbTNL7//nvD7FNK6F+TuXPn8t5776XbWFMqZ864I0yCg4NNZp/0wWr79u3Jmzevyb27du0iICCAlStX8u677wL/zZjcv38/wT4jIyN59OgRRYoUMZQVK1aMokWLEhAQkOTXr02bNly9epUjR45QtmxZo2yDSfHy8sLGxsYouNGLjY3l1q1bSe7ve/DgARcvXsTLy4tChQoZXVNKERERYRQYQlyQ27RpU5o2bWoou3HjBo8ePaJ27domfeiD64QOiX5ZSPAkMtfzR/BnV7h7zPz1Cm9Bu59kf5MQQqSztCZfeNXo//qtXyKWlJEjR+Lr68vRo0cZMmSI0bKut99+m/HjxzNr1iwGDRpkts2wsDB+//13AMObzd27d3Pjxg3efPNNfvvtN7P9xsTEMHfuXObOnZumc4DiW7ZsGTqdjtmzZ9O6dWuja/pzqF6c+UlsH5iePk31kSNHTIKn6Ohohg0bhpeXF5999lmKx5uc/vX0M3uPHj0yykD3/PlzVq9ejbOzM0uXLjX7Jn7x4sW88847+Pj4GIKnggULUqBAAY4ePUpYWJjZGa2DBw+i0+moUaOGUXmfPn0YNWoUEyZMMHz949u2bRvnzp2jaNGiJrOayWFlZUWNGjU4fPgwISEhRrN6hw8fJiwsLNGgHsDX15f333+fzz77jB9//NHo2tGjRwkPDzckfIiOjqZ06dJUqFDBsGRPT/+5ucyTjx49AjAJzl42WX3Ok3idPLkKc5smHDjV/QI6/iqBkxBCiAzl7++Pn58fOXLkoF69esm6x8rKCh8fHywtLfn11185ePCg4Zq3tzeDBw/m0aNHtGjRgsDAQKN7g4OD6dmzJ5cuXaJt27bUrx+3l1c/C9KzZ88E+9WfbzRv3jx0OnOJlVJHvzws/mzKjh07WLJkCWC8TE2f0CGhfTUA9evXp0iRIsydO9fo9QGYPHky06dPT3WygOT0r1euXDkgLqvei/766y+eP39Ox44dE5z96NSpEy4uLuzdu9covfc777xDUFAQn332mclesGfPnjFs2DAAk6Dxyy+/xMvLCx8fH8aPH29y75EjRwxf/ylTpqR6ad27775LZGSkYb8exH39Ro0aBUC/fv0Svb9NmzbY29szb948Ll68aPRs+pTvn376KRD3tShUqBCbNm0yWpJ45coVvvvuO/LkyWM2W+CZM2cA4zT/LyOZeRKZI+gmzGsFIYFmLmrQaipUT/wHWwghhEiJF1OVQ9wSprNnz+Lr60tsbCz/+9//UrSEqFKlSgwcOJDp06fz4YcfcuzYMcOb+okTJ/LgwQPmzZtHkSJFaN26NcWKFePOnTts3bqVhw8fUqdOHUPAFB4ezqpVq7C3tzecNWROnTp1KF68OJcuXWLz5s20atUqdS9GPN27d2fatGl88skn7N69m3z58nHq1Cm2bNmCu7s7Dx48MDpQV38+1JdffkmTJk2M3qTrWVpasnDhQlq0aEH9+vVp3749RYsW5ciRI+zcuZMiRYowadKkVI1X3/8vv/zCkydP+Pzzz8mfP7/Zuq1atULTNPbt22cUzOhf+169eiXYj4ODA927d+f333/Hx8eHKVOmAHEzj7t37+aPP/5g9+7dNG/eHDc3N27fvs2GDRt4/Pgx33zzDW+++aZRe/b29mzbto1mzZrx7bffMnfuXJo1a4aLiwtnzpxh27ZtQFxwGT+BRUr07duXefPmMWPGDE6fPk3VqlXZvHkzJ0+e5KuvvqJ8+f+OKggKCmLmzJm4ubkZ0r7nzp2b6dOn079/f6pVq0b37t2xtbXF19eXmzdvMnToUBo1amRo44cffqBWrVrUqlWLHj16EBUVxbJly4iIiMDX19fs7Nz+/fsBjJb5vZSUUq/NR9WqVdXLoOGCvqrc/HJGHxP9lqR/R1HhSo12Mf4Y55H+/YQ9VWpWddO+9P2dW5f+fSblh1KmYwm6nfnjEEKIdHbu3LmsHkKWGz16tAJMPqytrVWBAgVUp06dlJ+fn8l9np6eKu6tUcJCQ0MN9SZMmGByfcuWLapjx46qaNGiys7OTjk7O6s6deqoX3/9VcXExBjqLVmyRAGqW7duST7Pd999pwDVsWNHs9d37dqlANW7d+8k23rRtm3bVO3atZWLi4vKkSOHqly5spowYYIKDAxUFhYWqm7duoa6Dx8+VE2bNlV2dnYqZ86cKiQkRM2bN08BasaMGUbtnj17VvXo0UPlzp1b2draqqJFi6rPP/9c3bt3z6hegwYNFKCePn1qVH7t2jUFqPbt2xvKdDqd+vTTT5WLi4tydHRU27ZtS/TZqlevrgoVKqRiY2OVUkrdvn1bWVhYqHz58hnKEnLgwAEFqDx58qioqChDeWRkpPrxxx9V7dq1lYeHh7KxsVGFChVS7dq1U5s3b060zeDgYDVz5kxVs2ZNlTdvXmVtba3y5cun3nrrLeXv75/ovcn17NkzNXjwYFWgQAFlZ2enypYtq2bNmmXyvPrX19PT06SNjRs3qgYNGihHR0fl4OCgatSoof7880+z/fn7+6s333xT5ciRQ3l4eKj27durY8eOJTi+smXLqlKlSqXpGdNDcn9HAkeUmXhCU6nMZPIyqlatmjpy5EhWDyNJjRa+xyP1j1FZD6/hDG/QI4E7Uik6AibGy4ZiaQujUnauQ6JiomBxJ7i+1/SanRv0WAaeia/DzRDTSkPIXeOywefAtYD5+kII8ZI4f/58qvZNCPEqWbZsGT169GDr1q0v/0zHK+Dw4cPUqFEDHx8f3n///SwdS3J/R2qadlQpZXLSsux5EhlHKVj3mfnAySkvvLclawInIYQQQrzSunXrRokSJRJM0iAy1++//06hQoUMSTheZhI8iYyz6zs4tcy03MYJeq6E3KUyf0xCCCGEeOVZWFgwc+ZM/vrrL06dOpXVw3mtXblyhQULFjBt2jTDHsGXmQRPImMcWwR7ppiWa5bQbQHkq5D5YxJCCCHEa6Nly5b06dPHkAlPZI2RI0fSrl07unbtmtVDSReSbU+kv8s7wHeQ+WttZ0KxJpk5GiGEEEK8pubOnZvVQ3jtLV26NKuHkK5k5kmkr3unYUVv0MWYXqv/NVR5+de6CiGEEEKI15METyL9BN+BP7tBVIjptQrdodGIzB+TEEIIIYQQ6USW7Yn0EfEMlnQzTf8N4FUP2s0CTcv8caXVwwC4fRgKVJMEFyJTBUcGc+z+MaJ10VTOXRkPB4+sHpIQQgjx2pPgSaSdTger3oP7Z0yveZSC7ovByibzx5UWkaGw5Rs4FncaOZoldJoD5btk7bjEKysqNoqTD09y8O5BDt49yNnHZ1HEncOX0y4nvzX9jVI5JYAXQgghspIETyLtjs6Dy9tMy53yxKUkt3fL9CGlye0jsLofPLn6X5mKhT1TJXgS6UYpxeWgy3HBUuBBjt4/SnhMuNm6TyKesOzCMsbUHpO5gxRCCCGEEQmeRNoE3YJto03LrR3h7eXgVjjzx5Rauhjw+x52fx8XLMX3KCDzxyReKSFRIRy8e5B9d/ax/85+HoQ/SPa9l4IuZeDIhBBCCJEcEjyJ1FMqLiW5uQQRXeZC/sqZPqQ0WdINHl7I6lGIV4hSioCnAey9s5d9d/Zx4sEJYs0F5kIIIYR4KUjwJFLv5DK4vN20vMq7ULJl5o8nrSRwEukgLDqMA3cPsOf2nhTPLgkhhBAie5PgSaROyD3YPNS03DkfNJuQ+eMRIgvde36P3bd243fbD/9Af6J10alqx83WjRr5apDHIQ8Lzy1M51EKIYQQIq0keBIppxRs+BIigk2vtZkJdq6ZPqT0p8G/mc6EiE8pxYUnF/C75ceuW7s4/+R8qtqxtrCmSu4q1Mxfk1r5a1E6Z2ksNAtOPDghwZMQaTR//nz69u1r9pqtrS25cuWievXqDB06lJo1a2by6P4zc+ZMBg8ezLx58+jTp0+WjUMIkTwSPImUO7cGLvialpfvCiVbZPpw0p1bYejwK8xvldUjEdlIjC6GY/ePse3GNnbd2sX9sPupaie/Y37qFqhL3QJ1qZGvBg7WDuk8UiHEixo0aEDDhg2NyoKCgvD392fNmjX4+vqya9cu6tatmzUDFEK8VCR4Einz/DFs+Mq03MEdWnyf+eNJLTtX8wf6VuwBLaeAjWPmj0lkO9Gx0RwKPMT2m9vZdXMXTyOfprgNKwsrquapSr0C9ahXoB5FXIugvYwHRgvxkmrYsCFjxowxe+3bb79l/PjxDBkyhAMHDmTuwIQQLyUJnkTKbB4GYY9My1tNBcdcmT+e1CrTHna/sNTKzg3azIByneI+10lGtNdVeEw4B+4cYNvNbey+tZvQ6NAUt+Fu706Dgg2oX7A+NfPVlNklIbKpkSNHMnnyZA4ePEhYWBgODvKzKoRInEVWD0C8RC5uhtMrTMtLtYGyHTN/PGnRYCg0GQNF6kOtAdD/wH+Bk3jtRMZGsuPmDr7a/RUNljdgkN8gNlzdkKLAqUSOEnxY4UOWtFrCjq47GFN7DI0LN5bASYhszMbGBlfXuH26UVFRAERHR/O///2PmjVr4urqio2NDZ6ennz88cc8fPjQ6H5N0+jTpw8HDhygYcOGODk5kSNHDrp3787169dN+lu7di21atXC0dGRQoUKMXHiRGJjzf+xLiAggF69epEnTx5sbGwoWrQoQ4YMITjYeL9xnz59sLKy4vHjx/Tr1w8PDw+cnZ1p3rw5V65cITIykqFDh5I/f35cXFxo1KgRJ0+eTIdXT4jXk8w8ieQJD4o70yk+O1doPQ1etmVIFhZQd3Dch3gtReuiORx4mI3XNrLz5s4UzzBZWVjxRp43aFCoAQ0LNaSAU4EMGqkQIqMcPXqUR48eUbhwYdzc3ADo0aMHf/31F3Xr1uXDDz8kIiKCrVu38ttvv3H06FH++ecfkzYaNWpE3bp1+eSTT/D392fFihUcOXKEc+fOYWtrC4CPjw/9+vUjd+7cvPPOOzx//pyJEycagrcX+fv78+abbxIeHk7btm3x9vbm4MGDTJ06lfXr17N//35y5sxpqK+UolGjRsTGxtKnTx9OnTrF1q1badOmDcWKFeP06dN07dqVwMBAVq5cSevWrQkICJCZNiFSQYInkTzbRkFIoGl5i8ngnDfzxyNEKuiUjmP3j7H5+ma2Xt+a4j1MdpZ21ClQhyaeTWhQsAHONs4ZNFIhREZRShEcHMyBAwf4/PPPARg9ejQAhw4d4q+//qJnz54sXrzYcE9MTAxVqlThyJEjBAQEUKJECcO1M2fOMGXKFL7++mtD+y1atGDr1q3s2rWLFi1aEBQUxFdffUXBggU5ePAgBQsWBGDgwIHUr1/faHyxsbG88847REZGsmHDBlq0+C8R07Bhw/j+++/5+uuvmTt3rqFcp9Ph4ODA7t27DcFanTp1OHDgAJGRkZw+fRpn57jfV3379mX+/Pns3r2bli1fwjMZhchiEjyJpF3ZBcfMpE0u1iQuwYIQ2dylp5dYf2U9G65t4EFYyg6tdbR2pH7B+jT1bEqd/HVkGZ546fn5+bF79+5k1a1SpQpt27Y1Klu/fj3Hjh1L1v3mMt0tXbqUgICAFN2TFmPHjmXs2LFmr7m6ujJt2jTee+89AAoWLMj8+fOpV6+eUT0rKyvq1q3L6dOnefDggVHwZG9vz8CBAw2fa5pGy5Yt2bp1q2Hp3saNGwkODmbkyJGGwAmgWrVq9O7dm19//dVQduDAAS5dusQ777xjFDjpn2Xx4sX8+eefzJ492xAoAfTv39/o89q1a3PgwAE+/PBDQ+AEUKNGDebPn292WaEQImkSPInExUaDr5mlbTZOcWc6vWzL9cRr43H4YzZd28S6K+tSfA6Ti40LjQo1oqlnU2rmr4mtpW3SNwkhsqUXg7Fnz56xcuVKbt++Ta9evZgzZw729vaGugULFqR3797ExMRw7NgxLl68yJUrVzh+/Djbt28HMNmj5OnpiY2NjVGZfileZGQkgGGPUbVq1UzGV7t2baPg6cSJEwAmM1IQdz7VG2+8wZo1a7hw4QIVK1Y0XCtWrJhRXUfHuKyxRYoUMSq3s7MzGpsQImUkeBKJO/MXPL1mWt50LLgVyvzxCJGIyNhIdt/azbor69h3Zx+xKvlZEx2sHHiz8Ju0KNKCWvlqYW1pnYEjFUJklvipysePH0+rVq1YvHgxrq6uzJo1y6j+b7/9xrhx47h7N+44Czc3N2rWrEnp0qXx9/dHKeMD1F+c7dHTH0egr/v0adwS4RdngPRe3LsEcQEeYHYvFED+/PkBCAsLMyrXB0vxmRufECL1JHgSCdPpYN9M03LPOlD1vUwfjhDmKKU4+/gsqy+tZvP1zYREhST7XltLW+oXrE/LIi2pV6AedlZ2GThSIbKHhg0bpmlZXNu2bU2W8qVEjx5Zu9zb0dGRFStWULFiRX7++WfKly/PRx99BMDKlSv5+OOPqVChArNnz6ZKlSoUKhT3h8L+/fvj7++fqj5z5MgBYJIpDyA01DhZjT7AunPnjtm29IFYrlwv0fEgQrxCJHgSCbu0FR6aWe7UZExctjohstCzqGdsuLqBvwL+4uLTi8m+z0qzonaB2rQs0pJGhRrhaC0HIgvxusmTJw+//PILnTp14osvvqB58+Z4eXmxZMkSAJYsWULZsmWN7jl/Pu7/h/FnnpKjatWqAOzfv5/GjRsbXTty5IjR55UqVQJg3759DBo0yOiaTqdj3759ODk54enpmeJxCCHSToInkbB9M0zLPOtAoeqZPxYhiHvTcuLhCVYFrGLr9a1ExEYk+95yucrRtmhbWhZpSQ67HBk4SiHEy6Bjx4506tSJ1atX079/fzZt2mTYD3T//n2j4GnhwoWGJBvR0dEp7qtVq1Z4eHjw448/0r17d0PCiQsXLuDj42NUt27duhQrVozVq1ezceNGWrVqZbg2evRobt26xXvvvSfL8YTIIhI8CfNuHIRbh0zL6wzK9KEIERQRxLor6/jr0l9cDb6a7PtyO+SmrXdb2hZtS1G3ohk4QiHEy+jHH39k+/btbN68maVLl9KrVy+WLVtGx44d6dGjBy4uLhw+fJjdu3eTO3duHjx4wOPHj1Pcj5OTE7///jtdunShevXqdOnSBYhbJujh4UFQUJChroWFBQsWLKB58+aGJZJFixblwIEDHDp0iNKlSzN16tT0egmEECkka6+EeftnmpblLgvFm2b6UMTr68yjM4zYN4LGKxsz9cjUZAVO9lb2tPVuy5ymc9jaeSuDqg6SwEkIYVaBAgWYOHEiAIMGDaJ27dosW7aMokWLsnjxYubPn09ERAQ///wzmzdvBuLSjqdG+/bt2bFjB1WqVGH58uWsX7+eDz/8kO+++86kbu3atfnnn3/o3r07Bw4c4Oeff+bJkyeMHDmSw4cPmySZEEJkHi01a3dfVtWqVVPx1xZnR40WvscjZXyCeQ+v4QxvkM6bbKMjYGIe4zJLW/hwF/xS27R+Jx+o0DV9x5Bd6WJhXLz/OWkWMDplh6pmRzql40rQFXI75MbV1nw2p6wUFRvF1htbWXp+KacenUr2fRU8KtCleBeaeTV76fcxnXhwgnc2vWNUVsGjAn+2+jOLRiReJufPn6d06dJZPQwhhMiWkvs7UtO0o0opk/MFZNmeMLX/f6ZlboWhbMfMH4tINzqlw/eqLz8d/4l7z+9hoVkw5I0h9CzdM6uHBsD95/dZGbCSVQGreByRvGUxzjbOtCvajk7FO1EiR4mkbxBCCCGESAMJnoSx2Cg4vcq0vPbnYCnfLi8r/0B/fjjyAxeeXDCU6ZSOKf9MoY13myybgVJKcfzBcZZcWMKOGzuIUTHJuq9qnqp0Lt6Zpp5NJb24EEIIITKNvBsW8SiIf7CogztUyh6zE9mV0unQsmH69itBV5h+dDp7bu8xe12ndFx6eolqeU1Pvc9IsbpYtt/czvwz8znz+Eyy7nG1daVD0Q50KtEJb1fvDB6hEEIIIYQpCZ5E0mp8DDYOWT2KbOnIifn8eOInLqtI6ljnZGyHVTg45c7qYfEo/BGzT8zmr0t/oVO6ROsqMm/fY3hMOGsur2Hh2YXcDr2drHtK5SzF26XepmWRljLLJIQQQogsJcGTSJyNE1T/IKtHke0onY4lWz5l6v29xGoaaBqbY5+SY0NfhnffkGXjCo8MYdHhqcy9sYmwFJyBlNGeRDxh6YWlLLuwjKDIoCTrW2lWNPVsSo/SPajkUQlN0zJ+kEIIIYQQSZDgSSSuah+wlwNFXxQZEcS41Z1ZF/0A4r2pP/z8VhaNCnYemMLkC4sItMyyIZi4+ewmC88tZM3lNUTGRiZZ393ena4lutKlRBdyO2T9DJ4QQgghxIskeBIJs7CGWp9m9SiylXuBxxm0uS9nLWLNXo/MxCVwenfuHGbyjkH4qRBIJHDK55iPWBXLg7AHGT6mq0FX+e3Ub2y+vjnJZYMApXOWpnfZ3jTzbIa1pXWGj08IIYQQIjWyRfCkaVo/YAhQEDgBfKGUOphI/drAFKAi8BBYAHynlIrO+NG+Rip2B5f8WT2KbOOIrTVfbn6HJxbZYwlZdORz5m/pz5zHx4hIZExO1k70q9CPnqV78vG2jzM0eLr09BJzTs1hy/UtydpLVbdAXfqW7csbed+QpXlCpCOllPxMCSFEPOlxvm2WB0+apvUGfgXGAf8AnwFbNE2rqJS6ZqZ+UWArsA/oDJQEvgecga8ya9yvPg1qD8zqQWQLCljq7MTUXDmIySZvRvyPzWHiiVlcs1SQQOBkpRRd3avxcZPp5LTL2NPoLz65yG+nfmPbjW1J1rWysKJVkVb0KduH4jmKZ+i4hHgdWVpaEh0djY2NTVYPRQghspXo6GgsLdO2vyFLgyct7s9iY4E5Sqmx/5ZtAy4Cg4HPzdzWhbjFSZ2VUs+BrZqm5QMGaJr2tUqPkFJAqdbgIYeORmowIVdO1jg7ZfVQAHj08Dw/bP2EDTGPEl2iVzssnGFPnlKkUjPIwMDp/OPz/HryV3be2plkXSdrJ7qW6Mrbpd8mr2PeDBuTEK87Z2dnnj17hru7e1YPRQghspVnz57h7OycpjayeuapGOAJrNMXKKWiNU3bALRI4B5bIBoIf6HsMeD077Xsk2LsZVZ3cJZ0u/F0IL/vvYoGfNKwGE3K5MmScQA8sLRkUG53TtvZmr3upFOEZtISPl1sDKu2f8nMuzsISaTP3DExDHn8lGZh4WTkyAIub+In/0n4xTxNsm5Ou5z0LtubriW64myTtl9YQoik5cyZk5s3bwLg4uKCtbW1LOETQry2lFJER0fz7Nkznj59SuHChdPUXlYHT/qpjcvxyq8CRTVNs1Qq/omt/EncrNQkTdO+B4oCg4C/lVISOKUHr3pQMHMPTQXYevYen/x5zPD5x4uPsmlgPYrnyfw33PtvXGB8vjzcsTb/I1JF2TCo6mDePf59ho8l8O5RRm37GH8iElyiZ6kUPcNj+eRBII4ZOPl6585hfvYbhm/0A1QSb8bc7d15r9x7dCnRBXsr+wwbkxDCmK2tLYULF+bJkydcv36d2FjzCW6EEOJ1YWlpibOzM4ULF8bW1vwfxZMrq4Mnl3//DYlXHgJYAI7AsxcvKKWuaJr2FTCHuCQTAMeAvhk4zleTZgFoEH9jf91BmT6UByERDFt92qgsRqfYd/lRpgdP688fZvjBzyGBwKm7bUGGdlzFvQcnMnQcSqfj751DmHJrM88TmW2qqLNmVJ1xlDzrC/eXZshYnjy5zO/bB7M87BrR/55rlZDc9rl5r/x7dC7eWQ61FSKL2Nraki9fPvLly5fVQxFCiFdKVgdP+ndgCf2p3CTHsaZpHwC/Exc8LQfyE5dsYoOmaU2UUpHx6n8IfAikeZrulWNlAwWqwp0j/5UVqApF38zUYSilGLLqFE+eR5lcC4vK3L+Yzju6jWmnvkGzND2TyFopRhZsQacmP2T4OB7cP8OYLR+wVz1PcLbJVaf4olALOjSajIWlFZz1TfdxhIXeZ8H2L1jw9GRcAJdI0JTHIQ8flP+AjsU7YmuZtr/qCCGEEEJkR1kdPAX/+68zcP+FcmcgVikVauaeYcBGpdRH+gJN044A54GewB8vVlZKzSEu0KJatWqSTCK+jr/BinfgwTnIXwW6Lkj0DXJGWHzoBn4XH2Zqn+ZM3buSBZcnopk5w8k5Flxvt6NTn+8ydAxKp8N397dMur4m0b1NHW3yMLj5b+TIWTRDxhEd+ZyVu4bw293dPLHUEgzgAPLFxPBB0DM6DDyCjZUETUIIIYR4dWV18HTp33+9Md735A0EJHBPIeLOdTJQSl3QNO0xUCbdR/iqcy8GnxyEqDCwccj07i8/CGXixvOZ3m98Q7bMYWPgLDQL0/haRbkReOt97kV7ZOgYHj26wPhN77NT9yzBYCV3rGJc+Y+p88aADBmD0unYeWgqP1xYzG1LwDLhoClHbCz9gp7R/VkINgCWkhZZCCGEEK+27BA83QI6EHd2E5qmWQOtgQ0J3BMA1H6xQNO0YkAuwORcKJFMWRA4RcXoGLz8BBHRJqszM41Op6PfuqkcDl5sdsItNiIP4bfeQ8W4ZuiE3I79kxkTsJigRGZ42lnnZmiH+bi4FsqQMVy+spXv943kEOGJpkG31yneDX5Gn+BnOMnJAEIIIYR4jWRp8KSUUpqmTQZmaZr2FNgPDADcgRlgOBTXQyl16N/bxgErNE3zAZYCeYExwHVgYaY+gEiTH3dc4vSd4KQrZpCY2Fi6rRzOpciNZq/HhnkSdqs36DIusIyKDGH62rf5M/x6grNNuWIVo0v3oVGtjDkD+lnIXSbtacPysOvEJhIhWilFV3tPPnxzGu6zamTIWIQQQgghsrOsnnlCKTVb0zR7YCBxKchPAM2VUlf/rTIK6M2/ySWUUis1TesMjATeIW6v1DbgG6VU/Kx9Ips6cv0Js/3iZ6jPPGHRkbRfNpB7uv1mr7uo8ty52RVUxi1Fu3XrIF9v789ZM3us9Fpa5mR4u/m45SiSYeP4av8IYpLIoNfKKhcD6k+iUKFaGTYOIYQQQojsLsuDJwCl1DRgWgLX+gB94pWtBlZn+MBEhgiJiGbwihPosmjFV1RMDG2WfspD5W/2el6LOvzdbSblRu/IsDFs3zeJby/9mWBSiBw6xchib9Gs3sgMG4NeTCJBUw3s+KrmKEqVbJfh4xBCCCGEyO6yRfAkXi9j15/j1pNwk/IcDtY8DYvO0L51Oh3tlw9OMHAqbtuKFV2/Q9MsMqT/5CzTa6g5M6btfHK5lzB7PS2io5J3jnSBWPi6VC8a1/wazSJjXovMppQiMDgCGysL3J0kK6AQQgghUk6CJ5GpNp0OZNXR2ybllQu7Ud0rJ7/tuWrmrvSh0+nosuIbbsf4mb1e3bUnv7cbgoWFBbEZMC2W1DI9K6X4Mk9dejafne4Biy42ln9Wz8Ti8XGwt06wnr1O8WGuqrzT7Eds7VzTdQxZJTImlrXH7/L73qtcehCKhQYfNSjKkOYl0TI5Lb8QQgghXm4SPIlMc/9ZBN/8fdqk3MHGkpndK7H8n1sZ2n/vvycmmByiWZ5PmNaif4b1ndQyvQKxMLXGaMqX7Zrufd+4eJznqwZQI/oM8/J4AOaDpzZW7gxq8j/y5KmQfp1HhcHTa+BRCiwSSeGXAYLCovjT/ybz9l/nUeh/hx7rFPzid4XW5fNRrsCrESAKIYQQInNI8CQyhVKKr1edIsjMsrzRbcvgmcsxQ/vvv346J0JXmL1WP9f7GRY4xcZEMXNNd+Y/v5zgMr3GFi6M67AUV9fC6d7/7Z1zyBd8GhstBoA3IiLY72BvVKd0rCXDqw+hUrm306/j549hz1Q4Mhdio8ClALy7Lu5csQx283EYc/ddZcWR24RHJ5yM49zdZxI8CSGEECJFJHgSmWL9qUD2BDw0KW9WJg/dqmXMuUV6X2/5jX1P5pm9VtXlLX5uMyhD+n0eeo+hqzuyW4WavZ6Ry/T0Cj47/m+eyji9g0O4Z2XFZkcHcscobJ5WYkCnmVQqnid9OowOh0O/wL4ZEPnsv/Jnd2D7aHjrz/Tpx4zjN5/y+96rbD5zL1nJSHRyRpUQQgghUkiCJ5HhIqJj+X7TBZNyD2dbJneukKH7TsbtWsymwJ/NZuEuZd+WP9p/kyH93r17hAFb3ueShfkDgDNymV5irIARj59S/H45vovpRRDOfG6RTr8GTiyBXRPjAiVzAk+lTz8vUEqx//Jjftp5Cf9rT9K9fSGEEEKIF0nwJDLcH/uvcSfINLve953Lk9Mx485RmrH/b1bcmIqmmc4wFLZuzPIuE7DIgBmfE6f/ZOCRSTzJ5GV6959FkNT80XVdHobHvM8BXbl07RuAtZ8kfl2ZDyRTQynFjvMP+GnXZU7eCkq3doUQQgghEiPBk8hQj0Ijmb3rikl5o5IeNC6VTkvFzJh7ZAtzA8ahmZn5yWNRi7+7TcuQwOmhpnjv6CSizQROmlIMzPUG77Wem67L9MKjYvl+8wXKX35E5wRyMkQrS44U6MX0qA78c9s0kH1ZxOoUm84EMmvnZS7cS/pMbEcbS7q/UZhrj0LZddF02agQQgghREpI8CQy1IxtAYRGxhiVWVpoDG9VOsP6XHPuEDNOD0eziDG55kYl1r31EzZWGfOtH5nAbJO9TjGp5Du8WXtouvZ36nYQg5af4OrD5wyxymG2ToBVCaw6zKJWuRpY/HYQePmCp+hYHWtP3GW232WuPnyeZP08Lrb0rVOEHtUL42pvzdBV6b9kUAghhBCvHwmeRIYJuB/C0sM3Tcp7VC9E8TzOGdLnhYe3+fbQV2iWUSbXnHSl8e3xGw7WmXtAat5YxU91vqNUyXbp1mZMrI7Zflf4ccclYv7NjrA2tjbvWm7FSYs7CPe5suN0qYG80XUIlhkULCbIsw7c2J/mZqJjdaw+dpufdl7m9tOkg75iuZ34uEFR2lXMj43Vq3G4rxBCCCGyDwmeRIb5buN5k6xnzrZWDGpSIkP6C4kM5531A1CWwSbX7GK9WdfNB1c7hwzpOyHldVb82HYJ7h7pN9N27dFzBi8/wYl4e30uqsJ0jhpDO8sD5MzpTsOuA6hZqGi69WvEKS+E3jMtL1AVmo4Ht8IwM/X7qmJ1ivUn7zJzewDXH4clWb9sfhc+a1yMZmXyYpHA7F9SgsOiWfrPTQ5eeUxeFzsGNC5GoZyZ+/0ihBBCiOxNgieRIfYEPMTPzB6TTxsXw90p/Wd+dDod3VcNIcLSdH+VdWxB/u48Dw8nl3TtMzA4ItHrLS1zMq7bGuzszS+nSymlFEsO32SC7/kEzy+6ZumFfYsWdK/tleogIlneeD8us55ejiLQZDSU6QCaBkGpO/BYKcWWs/eYvi2AgPvmU7y/qEphNz5rXJyGJT1SnbXxTlA4f+y7xrLDN3ke9d/reujaY3Z80QArS5nBEkIIIUQcCZ5EuovVKSZuOG9SXsDNnj61vTKkz6+2/MKtGD8zg3Hij5a/UNA1Z7r2d/FeCKPXX4T85q9/4lqBj9stSrfEEEFhUXy18iTbzz9IsE7Z/C7M7F4pw5ZEGqn/NeQqBrf/gfyV44Imq9RnTlRK4XfxIdO2XeTMnWdJ1q9dNBcDGhejlneuVAdNAfdDGbTsOOtPBRJr5mCoG4/DOHv3GRULuaWqfSGEEEK8eiR4EuluxZFbXLxvmgltWMtS2FknkA4uDRYc28HW+3NMznJSyoJhVb6jUj6vdO3v5K0ges87THBYAQrn1vHkhb01tjrFhKLdaFH/23Tt75M/j5lN9w5gocEnDYvx+ZvFM2+fj6ZBuU5xH2l09MRR1u0+zLLAvESSeADWqKQHAxoXp6pn2mfz/th/Lck6zyKi09yPEEIIIV4dEjyJdBUaGcO0rRdNyisXdqNNhXzp3t/RO1f44cQINEvTlOQt8vanV6VG6dqf/9XHvL/gyL8ZBK3IEdiYiAI7CbOwoFCkwu1ZN1r0TZ/ASSnFokM3GO97juhY05kRgMI5HZjRvSJVPdN3Zi0zXDvrT8j64VSNOEJV4EPbXHSP+pbbysOkbr3i7nzRtASVC6cuaNJULG0sDtLI8gRhypY/YltyTaX/96MQQgghXm0SPIl09avfFR6Fmma6G9m6TKqXVyXkaVgo/TYPACvT1NWe1m/yQ4uP07U/nYJ3/zhMZMx/gdqJ0BZYXWxEbptbnIvypnBOp3TpKzQyhm9Wn2b9ybsJ1ulRvRAjW5fB0fbl+jF+ePc611Z8Q9Wnm7B84QDjAtpj+ln6Mjqmr6HsDa8cfNmsJDW9c6Wus9hoOL2SLy99h4fNf/uwmlkeoWnkFJ6RPl8vIYQQQrweXq53XSJbuxsUzu97r5qUt6mQL12WWb1Ip9PRedUgoq1um1yzjy3G8re+T9f+9F4MnPRisOVuVLF06+PivRD6/3k0wfOMXOys+KFrRZqVzZtufWYGXWwU/j5fUvHWIqprkWAmli5lERfgVCzoypfNSlKvuHvqgu6YSDixBPbNgKAbxJ/LyqMFUcviPFt0bwCQy9EGTcNs4C+EEEIIoSfBk0g3U7dcNAkubCwtGNqiVLr39eH6qTxU/iblWqwbi9r+jKNt5p7llF5WH7vN8L9PExFtGqQBlC/gyuyeVV7KFNoWzx9Q67mP2aBJz8Hakjndq9K0TJ7UBU3R4XBsIez/Hzy7k2hVNy2UIu6OfFCvCJ2rFOSDBUfYd/lRyvsUQgghxGtDgieRLk7eCuLv46ZvVvvW9Ur3N/qzDq3n0NM/TRNE6KyYUPMHSnokkAIvnRXKac+tJ0kf3JocEdGxjF1/lqWHE07x/U5NT0a2KY2tVfon3cguyhVwoXxqZtQiQ+HIH3DgJ3iecEbCF71T05PvWjfAMoUp3W88fs6JW0EU9XCibH6XdF+OmhUehT/ixIMT2FraUit/Laws5H8NQgghhDnyf0iRZkqZT02e09GGTxul33I2gAM3LvDb+XFoFqYJFLp5fUn7MjXStb+EVC+Sk2/blKHNT/vS3Nb9ZxH0W3iEU7dND/cFcLCxZFKn8rSvVCDNfWWWG0/C8EzFfVpi01LmREfEBU37psNz03PFElMuv0tcqsJkUEpx5MZTftt9le3n7xvKv2pWggGNi6eo3+wiKjaK3bd3s+7yOvbe2UusijvjqkbeGvzS5BesLa2zeIRCCCFE9iPBk0izg1cec/j6E5PywU2K42KXfm/AomJiGLhjKFiaHk5b2r4d3zbqlW59JaZRSQ9+6VWV+88SPyQ3Oc7cCeaDBUe4l0BbxXM78UuvKhTLnQlnN6WD55ExzNp1mQ17D7MnkS99lLLipusbFHt2MHUdxUTBicWweyqEJJxUw8DGGaJM0+cnJVan2HzmHr/tucLxm0Em13/ceZkP6nlnSAr+jKCU4tzjc6y9spaN1zYSHGkasPvf8+ef+/9QO3/tLBihEEIIkb1J8CTS7I/9103KiuV2okf1wunazycbphFhaZqQwlmVY3Gnsenal4UG+VztCAw2Dmpal8/HjO6V0uU8pa1n7zFw2QnCo2PNXu9QKT/fdSqPg032/zFVSuF7KpCJG85z71kEdjgRaWWFrRZjUveocyPydpxEMYsnML9VyjrSxcKpFeA3CYJuJF2/dNu4A30P/w7HF6WsL+Dzpcd5FmH6DHpRMToCgyMo4u6Y4rYz08Owh/he9WXdlXVcDrqcZP1bz24leAC0EEII8TrL/u/KRLZ2/dFzdly4b1I+uEkJrCzT78DWjRePcujpUpN9ThYxuVjeeRY2Vun7raxpGn1qezFp0wVDWfdqhfiuU/kU75GJTynF73uvMmnTBZSZ45tsLC0Y3a4Mb1cv/FLsp7l4L4TR685w6Op/s48R2LIsthG9rbYZys5bl8Wi+QSqVmscV3B9f/I70eng3Jq4oOlRQOJ1NQso1xnqfQm5S6fgSUwlFjhld9G6aPbe3svqS6vZe2cvOmU+CYkQQgghkk+CJ5Em8w9cNwkACrjZ07xsnnTrIyQynBH7RqBZGc/QKKUxpNoYCrml8gygJHzUoCgl8jhz+k4wb3jlpFbRtPcTFaNj1JozLD9iPjGEu5MNc96tRpVUHgabmZ5FRDNz2yUWHLxOrM40ChwT05vDutJUsw+kbK2WvNG4I5pFCgNqpeDydtg+Fu6fTqKyBhW6Qf0h4J6+e+1eJjef3WT1pdWsvbKWR+GSPVAIIYRITxI8iVQLiYhm1VHTc5bereWZrrNO/dZ9R4yVaSa/Mg5t6VmxYbr1Y06jUrlpVCp3urQVFBbFx4uPGs3QvKhUXmd8elejYI7snYZcKcWG04GMWXeOR6GRCdaztrKiaP1evNWwGPY2qdgTFHgKto2Cq35J1y3dDhoNT/NM08sqMjaS7Te2s/rSag7fO5zs+2wtbXG2cZYgSwghhEgmCZ5Eqq04cpvQSONlTfbWlrz1RvrtdfK78Q/nWWuyXM8qJj+/txuRbv1ktKsPQ3l/wRGuPTJ/8G3jUrn5sUdlnGyz94/k3aBwRq05w44LiacDb1I6N6PalMEzVyr2AgXfhp0T4OQywMy6xhcVbxYXNOWvnPJ+4nF3sknwWr3i7nxUvygj1pzmxuOwNPeVXi4/vczKgJX4XvXlWdSzZN9XJXcV2hVtRzOvZsw8OpMVASsycJRCCCHEqyN7v1MT2VasTjH/wDWT8i5VC+LqkE4Z9rQozkX/hoWN8RtopSwYV3sCrnbZe4ZG7+CVx3y8+CjB4dFmr79ftwjDW5VO816qjKTTKRb73+D7TRd4HmU+wQWAZy4HRrctQ+NSqVy2ef8s/FQVYpLIZOhVDxqPhMI1U9ePGV2rFWL9qUDDEkRLC422FfLRr743ZfO7Aome75tpomKj2H5jO8svLufYg2PJvi+vY17aF21Pu6LtKOySvslchBBCiNeFBE8iVbafv2/2gNg+dbzSrQ/b3BuxsHlsUl7d7S3aln4j3frJSFvP3mPA0uNExZhu1re00Bjbriy9aqbmRKTME3A/hGF/neKYmVTdenbWFnzWuDjv1y2StrTdZlJnGylQDd4cBd4NU99HAuoUc2fZhzXZcCqQ3C62tKuYP1stoQyJCmHG0RmsubyGJxHml37GZ6VZ0ahwIzoX70zNfDWxtHg5UqoLIYQQ2ZUETyJV5u03nXVqWNKDoh5O6dK+pWMANjkPmZTbxhZhdusv06WPjPb38dt8tfKU2WQKznZW/NKzKnWLu2fByJInMiaW2buuMNvvMtGxCS+fa1YmD6PblaWAm33GDSanNzQZE7e3KQMzEL7hlZM3vHJmWPtpcS34GteCTX/uzPFy8aJz8c60LdqWXPYZk1BFCCGEeB1J8CRS7OzdYLNJD96rUyR9OrAIwy7fKpNipbNmeqPJ2FknvDclu1h08Dqj1p41e80zlwNze79BsdzpE2hmhKM3njD0r9NcfhCaYB0PZ1vGty9Li3L5Mm4g9jmhwVCo9h5YZf+ve1ays7SjmVczOhfvTOXclV+KNPdCCCHEy0aCJ5Fi8xI4FLdeOs2i2OVdh4W16eb3N/O8R/0iZdKlj4z0867LTN1y0ey1yoXdmNv7DXI6Zs9AICI6lunbAvh971WzZ1Dp9ahemGEtS+Fqn4b9bZaJvAaWtlDzY6j7Bdi7pb6P10Axt2J0K9mNNt5tcLZxzurhCCGEEK80CZ5EijwKjWTdibsm5X3reKXLX7qtnE9j7XrCpNxJV5ppzfunuf2MdutpWIKBU91i7vz2TlUcs2lGvXN3nzF4+Qku3g9JsE4Rd0cmdSpPTe90WAqWtzxYO0J0vAyEFbrHJYNwe32TGtha2iZ63crCiqaeTelesjtVcleRWSYhhBAik2TPd3Ei2/rz0E2iYo2TH7jaW9OpcsE0tx2uC8Y279+mF2Lt+KXlFKwss/9m94Rma5qXzcOPPSpja5X9nkGnFLP9LjNjW0CCe5usLDQ+auDNZ42Lpy0hxIus7eCtxbB+EIQEQpEG0HhEuqQdf9kVy1EMN1s3giKDjMoLOBWgS4kudCzWUfYyCSGEEFlAgieRbJExsSw6dMOk/O0ahVN3CGo8J0KXYmFleoZOh8KfUimfV5rbzyqdqxTk+87l0/Xg4PT01cqTBAYnnBq8YkFXJneuQOl8LunfedHGMPAk6GLAMp1S3L8CrC2smfXmLCb5T+Ju6F0qelSka8mu1MlfRzLmCSGEEFlIgieRbBtOBfIoNNKozNJC491aaU+1feHJBS6H7zQpz0kVxjZ+N83tZ5U+tb34tk0ZLLLxGU4JBU7WlhpfNC3Jh/W9M/YMKk2TwMmMih4VWdZmWVYPQwghhBAvkOBJJItSirn7TNMktyyXl3yuaUtRrZTi+8PfA8ZLxjSdPT5tJ2NhkT1nbJLyeeNiDG5a4qXcj1IqrzPTu1WiTP4MmG0SQgghhHhJSfAkkuWf6085e9c0A957ddOennznzZ0cuX/EpHxA5f4Ud8/ANNgZaESr0vSr753Vw0gxTYMP63nzRbMS2XJ/1stCKcWDkEg8nGyz9ayjEEIIIVJGgieRLOYOxa1UyI0qhXOkqd2o2Ch+OPKDSbmXixd9y/dKU9sZLZ+rPc62VoRExhjKNA0mdSzPW9VfvkxxBXPYM61rRWqkRya919SziGgWHbzB/APXeRgSSX5XO37vXY2y+V2zemhCCCGESAcv53ookaluPQljy9lAINaovG8drzS3vfj8Ym6H3jYp/6raV1hn830wNlYW/y7Li/vcydaKn3pUztaBk4ez+RTY3asVYtPAehI4pdKj0EimbL5AnUk7mbrlIg9D4vYG3g2OYOhfp7J4dJkrWheNSuyQMCGEEOIllqaZJ03TbABbwOy6FKWU6Tov8dKZtmczdl6zsbB5ROzz4oTffYs8Tq60Kp+2JXWPwh8x59Qck/Ja+WpRv2D9NLWdWd6rW4Q6xdy59ug5Nb1z4uaQPQ+/1etStSCbztwjVhf35jaXow2TO1egaZk8WTyyl9cPWy6y/fx9ImN0Zq+fvfuM6Fgd1tk022J6eBrxlI3XNrL28louPr1IAacCTKgzgSp5qmT10IQQQoh0leLgSdM0C2As8D6Q2DsulZr2RfYSFP6cHY9nYGkXBICV8wVsPbbwbqUhaX4zOOv4LJ7HOyDVQrNgyBtDXqokCyXzOlMyr3NWDyNZGpbMzeL3a7D5TCAFctjTuUpBcjklfiCrSNyG04GJXlcq4fO/XmaxulgO3D3A35f/ZtetXcTo/lu+eivkFoP9BrOr2y4stFc3aBRCCPH6SU1wMwwYAUQDp4Bg4qdJE6+MuUc3g1WQUZmV4zXeTuPStAtPLrD60mqT8q4lulIsR7E0tS0SV6toLmoVleV5InVuPLvBmstrWHdlHQ/CHiRY70nEE26F3MLTJe1HGQghhBDZRWqCp/eAQKC2Usr0xFTxStl8batJmZOdRg7H1C9P06cmV/FibmcbZz6t9Gmq2xVCZIzwmHC2Xt/K6kurOfbgWLLve3E2SgghhHgVpCZ4KgjMksDp1RcSGU5gzFHir7pxsk3baswdN3eYTU3ev2J/ctilLXufEBkpsaWqjjaW9Krpic++a4Y9ZS+7gKcBrApYhe8VX0KiQ7J6OEIIIUSWS8274JuAW3oOQtO0fsAQ4gKzE8AXSqmDCdS9DiS0DmSMUmpseo7tdTbv6FY0i0iTcnub1J//k1hq8rdKvpXqdoXIDNW8cnLpQahRmZuDNX1rF6F3bU/cHGz4Y/+1eHkpM9DzxxCwCcKDoEw7cEufTI8H7h5g3dV1nHr4emUKFEIIIZKSmuBpDvCtpmkTlVKmh/+kkKZpvYFfgXHAP8BnwBZN0yom0H5H4jL8vegLoCWwLK3jEf/ZcHWL2fK0pHJYfH4xd0LvmJS/DKnJxWvgyTW46gcuBaBoY7A0/hX5TatS3H8WwZ6AhxTK6UDPGoXpUb0wjmmcjU2xBxfA/xc4uQxiIuLK9kyFD7aDe/E0N7/z1s5k1bPSrKhfsD4dinVg2tFp3HgmCxKEEEK82lLzf/xjwEXgqKZpq4BLgOn0BKCU+jGxhrS4lGpjgTn6GSNN07b92/5g4HMzbR6P10Y14gKqD5VSF1P8NMKs55GR3I06AqmfZDKRUGry2vlrvzSpycUrKugW7J4MJ5aA+jfleLnO0OUPo2oudtb80ecNYmJ1WGV26nGl4MoOODg77t/4IoLg+CJoOi7Dh1LUtSgdi3ektXdr3O3dAZh5bGaG9yuEEEJktdQET9tf+O8PEqmngESDJ6AYcUvw1hluUipa07QNQItkjudH4DAwP5n1RTIsPLkDLMPTtc2EUpN/Xe3rlyo1uXiFPH8Ee6fDPz4QG+9vQGf+gsajIGcRk9sSC5yqahfoY72R8hZXOaErysjo99I2xuhwOLUcDv0CDy8kXjfoVtr6SoS9lT0tvFrQuURnKrhXkJ9ZIYQQr6XUBE9907H/Ev/+ezle+VWgqKZplkqpBLcPaJrWHqhFXOa/V2OHdjax/tKmdG3v0tNLkppcZC+nlsOWERCVSCKEp9fMBk9mXd8HfpNZZrXXUFTA8jH2RAHdUz6+0AdweA4c+QPCHqf8/mRytHZM9HqpnKXoUrwLrbxb4WzzcpxnJoQQQmSUFAdPSqkF6di/y7//xn/3EgJYAI7As0TuHwzsSyi5hEidiOgobkUeTtcle3+c+UNSk4vs5cb+tLehFFzbDbunJNheHYszoIsl7ldaMjy+Agd+iltCGH82LAPUL1if+WfnG/182lvZ06pIK7qW6EqZXGVklkkIIYT4V6p3Of+7X6keUBFwAB4DZ1MYyOj/j5zQrJEukf5LAg2ArkmM80PgQ4DChdMnE9WrbslJP7AMS7f27obeZdM105msjyp8JKnJxctJKbiyMy5ounUo0aq2WgxRKhZIIiHKnaOw/39wbh3JOnfc2gGi0/5zWi1vNX5o8ANLLyzFzsqOxoUb06pIqyRnpIQQQojXUaqCJ03T3gAWAfq0ToYgSNO0S0AvpZTpQT6mgv/91xm4/0K5MxCrlAo1vcWgPRAK+CbWgVJqDnEZAqlWrZos7UuGv9N5yd6CswuIjbf6MqddTrqXTMVSJiGyklJweTv4TYY7yfkVl5z2dsD+mXB9b5LVAfCsC7U+gajnsLpf2scANPNqRjOvZunSlhBCCPEqS3HwpGlacWAbcQHOX8A+4C6Qg7iZoG7EpRqvloxU5pf+/dcb431P3kBAEve2ADYppSJS9gQiMVExMVwP90+3JXtPIp6Y3evUs3RP7Kzs0qcTIZIS/6TnF1lYQ9U+cPsfCDyRcL1re2HneLjln/bxxMbA2dWwbyY8OJt0fQvruOx/NftD/kpxZadXpX0cQgghhEiR1Mw8jSZuL1JrpdTmeNd+1zRtMXGzQcOBpP4segm4BXQAtgJommYNtAY2JHTTv0sGqwFjUj58kZjlp/eAZSIb6FNoyfklRMQax7cOVg4y6yQyV76KZgo1qNANGn4TlxRiYXvz994+CjvHxZ3/lBSP0ugeXMBCS2CSOyYSTi6FfTPg6fWk27N1gWrvQY2PwSVf0vWFEEIIkaFSEzw1AdabCZwAUEpt1jRtHdA8qYaUUkrTtMnALE3TngL7gQGAOzADQNO0ooCHUurFjQWexM18yblO6eyvi+m3ZC8sOoylF5aalHct0RVXW9d060eIJFXqCQFb4NK/Bz+XaAFvfgt5yiZ+35YR8OBc0u3nLgsNhkDpdsSMdceGeElCo8PhxIK4PU3PTA+JNuGcD2p+EjcjZueSZHUhhBBCZI7UBE85iEslnpirQMvkNKaUmq1pmj0wkLjseSeA5kopfR+jgN78t68KIPe//wYlb8giOWJiY7kadjDdluytCljFsyjjZIlWFla8U+ad9OlAiOSytoOeK+LOQbJxBIecybsvqcApb3loMBRKtgaLhJcGWs+uBmGPku7PvSTU+RzKdwMrm+SNUQghhBCZJjXB0y3izlZKTG3i9kEli1JqGjAtgWt9gD7xyg5jHEyJdPDX2QMoy+CkKyZDdGw0C86ZZrVv692WPI550qUPIVLMrVD6tJO7DDQaAaVaQzLSeGtJBU6FakLdQVC8eaJBmBBCCCGyVmqCp9XAl5qmjVFKjXnxwr/7lcYCNUggGBLZ14rzptvMbJQ7UVoy/mIez4ZrG3gQ9sCoTEOjT7k+qR2eEFkvZ1FoNBzKdkqfIKdYU6j/FRSumfa2hBBCCJHhUhM8TQDaAaM0TXuXuGx7wUAB4I1//70ITEyvQYqMp9PpuBR6wOQ7oqp7Aw4+/itlbSkdf5z5w6T8zcJv4u3qnZZhCpFxLBNZJudSEBoOhYpvg2Wqj8f7T+m2UO9LyF857W29hEKiQnCydpLDd4UQQrx0UvwuQCn1TNO02sBU4C2g1wuXI4B5wBClVPqs/xKZYs15f5TVU6MypSzoWb4VB/1SFjzturWLa8GmWerfK/demsYoRIYqXAsubTUuc8wdF+RU6wtWtslqJgRHcvHM9IJmEZduvO4XkKdMOgz45XI39C5/X/6bNZfXcO/5PTxdPJnWYBolc5bM6qEJIYQQyZaqP6EqpZ4A72ua9jFQEnABQoCLSqmodByfyCTLz5ku2XNWJSnk5p6idpRS/HHadNapet7qlPcon+rxCZHhan8Gj6/Amb/AOQ9U6Q01PopLMJECG3Q1edfyvyAsWlliUakHlvW/gFxF03vU2VqMLobtN7az6tIqDtw5gOK/FO43nt1g5P6RrGy7MgtHKIQQQqRMmtafKKWigTPpNBaRRXQ6HRee7Tf5bqiTr3GK2zpy/winHp0yKX+/3PupHZ4QmcPSGjr8DO1+BIvUp5ycpHuH+zo33rC4yAVVmEUxTdjV9l0srYz3SF19GMrlB6FU88pJTsdXM7Ne7829eR79PMHrF55cIDI2ElvL5M3qCSGEEFktyeBJ07RjwK9KqTkvfJ4cSilVNS2DE5lj06Vj6KyMk0IopdGvajviVmIm39wzc03KSucsTa38SSVoFCKbSEPgBBCDFT/HdiD+UU96R288Yeb2S+y9FPczZ2dtwaL3a/CGVzLTp79EEguc9HRKlwkjEUIIIdJHcmaeKgF5432eHCrpKiI7+PO0r0mZo644JT3yczU4qSO9/nPhyQX239lvUv5eufdkY7h47R25/oT/7fgvaNKLiNYxc3sAf34gGfeEEEKI7C7J4EkpZZHY5+LlptPpOBe81+Q7oWbeRiluy9xep0LOhWji2SS1wxPilfDuH/4cuvokweuX7odm4miEEEIIkVoSCL3mdl49TazVA5PyD6u2T1E7t0JuseXGFpPyPmX7YGWRDqmdhXiJJRY4vSo8XTzNlltZ/L+9+45vszr7P/45kuW94h3bcRJn771YSUjYs0BLN20Z3XQ8/XUvaPu0T/vQ0paOhw5KS6GlixVWCATIXmTv4ew4nvFe0vn9cSuJbXnIipec7/v10kv2uc996xIRsi6dc64TwdVDr+bRqx7V2iYREQl7IX2qNca4gauttS/5f4/E2Rz3cqAA+JG1VoUkelBdo5dHXt/PC1tPkJUUzX+/axL56fFdvs6ftz4f0BbjHcGEzCFdus7jOx4PWLuQGp3KLSO7loSJCDR6ffznneP8ff1RTpbXct2kwXzturFEuLv2fVdRZT1/Xl3AKztO0eSzfPyKfO6cldcjMX988sdZc3INtU21AAxLHMbto27nphE3kRqTCjgbZYuIiISzLidPxphM4A1gjDFmsLX2NPAL4F7AAJcANxlj5lprd3VrtHLOX9ce4ZE39gNQUFLDh/6wjhVfWdjltUXbygKn7M1Mn9+la5TWlfKfff8JaP/g+A/qm2aRLqhv8vKPDcf4zfIDHC+vPdf+hxWHSImL5NMLRwZ1nQNFVfz+7YP8a9NxGprOf6nxlX9tY0R6PDN7oDjFhLQJvHr7q6wvXE92XDbjU8drraOIiAw4oUzb+zYwFvg1UGuMSQY+AhwG8oD5/ut+u3tClLY8v+VEi9+Pl9eyt4vrJnYXHaMp4kRA+70zbu3SdV48+CINvpbbe8V54njPmPd06ToiA4Gng9Gh/LQ4vnH9uID2Jp/ljysOccWP3+Cbz2xvkTidtaJVoYm2lFQ3cM/jG1j00Js8te5oi8Tp3HX2d36dUCVHJ3PV0KuYkDZBiZOIiAxIoSRP1wNLrLWftdZW+n+PBB6z1h6z1r4NPA10veKABO3UmcAS4jUNTV26xnO7VgW0RTTlMC17eJeu8+yBZwPabh91O4mRiV26jshAMHt44KhOfnocD985laVfnM/NU7MDjpdWN/DgCzsprKhv97p1Te3UPm9m1YFiXttV2GGfukaVBhcREQlVKGueBgNPNvv9Opyy5C83azsN6JNzD/H5LMVV7X/ICtaaExsD2vJiJ3TpGrtLd7O7dHdA+22jbgs5LpFw9uPbJ/O5v21m4+Eyxg5O4O7LhnPj5Gzcru4diWnw+hiYW+uKiIj0X6EkT6eAbABjjAu4BigFNjTrMxk4dsHRSZvKaxtp8l34NlqHa3ZAq/1AZw3u2r7Gz+4PHHWalDaJEckjLiQ0kbCVkRjNU/fNxeezuLo5YQKoqm/iybWHOfzmTn7Q7VcXERGRjoSSPK0D7jDGvAHMBNKAP1hrrTEmHvgEcC3wm+4LU5orqrzwUaeiqgrqXUcDal/dPPaSoK/R6GvkxUMvBrTfMkIV9kQuJHGakpvElmNnWrRV1zfx06V7eXxVAWdqG7nJ1UhHQ085yTFkJ0ezvqAs5DhERESkpVCSp68Bc4DHcKrrlQDf9x/7H+CTwAHQl6I9pTuSp+d2r8aYlmsfTFMKk7OGBX2NFcdWUFrXcv8aj8vDtcOvveD4RAaqtPgokmI8nKltbNHuMnDzlGw+tXAklXVN3P6blmsS9xZWsbdwX6fXn5CdyH1X5HPDpME8+vZBJU8iIiLdqMvJk7X2gDFmBvBenOTpn9bak/7DL+Ps8/Q7a215dwUpLRVVBRaL6Kq3jmwIaMuMHNula7RVKGLhkIUkRSWFHJfIQOd2Gb5x/Ti+/K+tAES4DLdNz+GTC0YyPC0OgI2HQ0t45uWncuNHLlOlOxERkR4S0ia51tpi4JE22p8HAnddlW7VHSNP+85sDai1OCltatDnl9WV8eaxNwPatSmuSOfeM2sIl45KY++pSibmJJGe0D37oaXFR4ESJxERkR7TafJkjLkZ2G2t3dvs96BYa5+7gNikHReaPNU1NlDBgYD1TteMnBv0NV489CJNvpal0dNi0rgkO/g1UyIXs5zkGHKSY9o81ln+43Eb5uWnwtEeCOwCHC2tYcuxcoanxTF+cGKXR8BqGmt48dCLPH/geY5UHmHO4Dl8a+63iPPE9VDEIiIiXRPMyNMzwHeBB5v93lmpN+Pv4+6kn4SguKqh804dWLp/M8bVKgHzRrMof3LQ12iryt5N+TcR4QppMFNEmhmZEY/LQOuimjEeN++fk8c9lw9n8JGafpE8+XyWN/cV8fiqApbvKTrX/vnFo/j84tFBXWN36W7+secfLDm0hOrG6nPtSw4uwePy8L1Lv9ftcYuIiIQimE+6DwDN52c9SOfJk/SgCx15evXgmoC2JNcoItzB5bonq09S7w2M4eYRQQ9KikgHEqM9fP36cfzgxV1YC4nREXzk0uF85JJhpMT1j92dKuoa+eeGY/xlzWEOFVcHHP/VG/u574p8YiM7/jNz10t3sat0V7vH15wMfL8SERHpK50mT9baB1r9/t0ei0aCcqHJ0/aSzQFtY5KnBH1+W4nThNQJjBw08kLCEpFm7rk8n5unZHOsvJbxgxOJ9vSPgfz9pyt5fNVh/r3pGNUN3nb7NXotx8tqGZWZ0OH1OkqcAOqbLnyNp4iISHcJaY6VMSYOuBfYYa1d2qz9ZWAp8HNrbVN758uFKaoK/cOEz+ejuGlPwITKhcNmX1BMKhQh0v0yEqPJSIzu6zDw+Sxv7DnNYysLWLG/uMvnNzT5eGXHKeqbvAQsthQREQkjXU6ejDFpwOvABODHOMkSxphY4DLgKuB2Y8w11trKboxVgEavj9Lq0Nc8bTxxENwVLdqsz81NY+eEfE2Py8N1w64L+XwR6Z9qGpr418ZjPLaygINtTM3rzMkzdTy/5QRPrT9KUWU98WNUDFBERMJbKCNP3wUmAl8Hfnm20VpbY4wZBHweZ7Pc7/l/lm5UcoHFIl7YszKgLcYOJSk6NuRrLhiygOTo5AuISkT6k1Nn6nh8dQFPrj0SsJlve9wug7dVhYsP/3Fdi99tUwImsuXG2meNSxnH1cOu5uebfh5a0CIiIr0glOTpeuBZa+2PWh+w1jYCPzHGzAfuQMlTt7vQ9U4bCjcFtA2Pn3hB17xlhKbsiQwE246d4Q8rDvLC1pM0tS7114a4SDe3z8jlw/OG8YknNrL/dFWH/RvK5hGdueTc79YXya2jbuB9Y+9kfOp4yurLlDyJiEi/FkrylAkc6KTPLpzpe9LNiqrqLuj8E7U7A/7V5+XMDPl6qdGpXJKjvZ1EwpXPZ3ltVyG/f/sQ6wraHhVqbXhaHB+eN5TbZ+SSGO0J+rEaSy/D15BKRGwB3vp0mion8Y0P3EpMZP8ohiEiItKZUJKnIzhrmzoyBzgWwrWlExcy8lRQepqmiFMB7beMCz35uTH/Rjyu4D88iUj/UNfo5Zl3jvPo2wc5WBTceqbLRqZx92XDmT86HZer5eKlzpYyRbgMTT7wVo3HWzU+xKihrK6M5w48x4uHXuR0zWkW5S3iK7O/ovchERHpFaEkT08D3zTG/C/wdWvtuUU4xpgI4NvApcBPuidEae5Ckqdndq8KaHM3ZZGfkhnyNW8eqb2dRMJJRV0jv3pjP39aVRDU+0mk28Wt07L52GXDGZuV2G6/0ZkJ7Gtj2l5mYhTvnz2U980ewhU/eYO6Rl+XY7bWsv7Uev6595+8duQ1Gn3n12H9fc/fSYlO4VNTP9Xl6wbj0JlDrD+1Ho/Lw9XDribOE9cjjyMiIuEhlOTpR8DNwBeBu40xm4EKIAGYCiQDW4Hvd0uE0sKFJE+rj28IaMuJGRfy9caljGP0oNEhny8ive/JtUeC6pcaF8kH5w7lg3OHkp4Q1Wn/L149mrWHSin2b6UwLz+VD80bylXjM/G4XSHHW9VYxU3P3MThisPt9llzck23Jk+VDZW8XPAyz+5/li1FW861P7X7KR6/7nFiImK67bFERCS8dDl5stbWGmMuAb4GvBeY3+zwUeA3wH9ba7te11Y6dSF7PB2q3B6wv9P0jOkhX097O4kMPKMz47n7suHcMjWnSxvzjkiPZ+VXF7L12BlykmPITu6eBKPR19hh4gRtb9zdVV6fl7Wn1vLs/mdZdmRZm9fcVbqLNSfWsDBv4QU/noiIhKeQNsm11tbiTM/7tjEmBhgEVFlrKzo+Uy5UqCNPZ+pqqDEFAesSbhg9L6TrRbgiuH749SGdKyL9zyUjUrnvinzmj07HhLgZU1SEm1nDUkI690xNI39dH9yoWHc6XHGYZ/c/y/MHn+dUdeCa0NZOVJ/ohahERKS/Cil5OssYEw9MAlKstUuMMYOstWXdE5q0JdTk6YU96zAub8tGbwKzc0eFdL0FuQsYFD0opHNFpH9wGbh+0mA+fsUIJuUm9UkM246f4Z8bj/LclhPU+yqI74WZwFUNVbx6+FWe2f8M75x+p+cfUEREBoyQkidjTCbwc+A2nIlg1n+tTxljPgp81Fr7drdFKeeEmjy9UbA2oC0tYgwuV2hrEW4eoUIRIv3diPT4NtujPS7unDmEey7PZ0hK6Btkd4f3/N/qZr/FYH0ejKvtjXnnDp7L1Iyp/HbLb7v8OD7rY92pdTy7/1leO/wadd4L2/ZBREQuTl1Onowx6cBqYBiwEogGzi6cqQaGAi8ZY+ZZa7d1U5wC1DQ0Ud3g7bxjG3aXbQ2oJTwhZWqn5yVFBn4bnRKdwmW5nVWrF5G+tmBMOpeMSGXVgRIAUuIiuWveMD40bygpcZF9HF1b3DSWzSMy9a3zTd547p5yJ7ePup0hiUPYUbyjS8nT0YqjPHvgWZ478Bwnq08GfV5SVDJuG0NpQ/DniIjIwBfKyNMDQB5ws7X2BWPMd/AnT9bah/3V914Gvgnc2V2BChRXNnTeqQ1NXi9nfPsCikVclT+303NTY1KZkzWHtafOj1zdN/k+7akiEgaiItz85e45rDtUisUybcigfr8hbf3pa/HW5uGKOomvLpsEO4nPf+y6Ll2jtqmWpYeX8u99/2Zj4cagz3MbN1NT5+KpncP6nZnUJjxDZErL5Kmwou0RK2stQMjrxUREJDyEkjzdDPzbWvtCWwettcuNMf+m8410pYuKqkKbZrL80HZw17Zos75Irhk1Lajzf37lz/ntlt9y6MwhFgxZwO2jbg8pDhHpfW6XYd6I1L4OAwBXJ4lFjMdNbSM0VU6EyokAmLjg/0ztKd3DP/f+kyUHl1DZWBn0ecMTR5DnuYL9h8awfOfZVktUQmDfo6Xn30trGpp4Zccp/r3pOJsOl5ESH8l3bpzA4vGh750nIiL9WyjJUxpwsJM+x4D0EK4tHQh1vdPL+1cHtCUwgmhPcNN24jxx/NfM/wrpsUVEzho/OJENhwNrCk3MSeRDc4dyyYg0Lv/xG12+7qnqU7zvhfexvWR70OckRSYxZdBCKoqnsnZDDFu9NqjzmnyWlfuL+fem47y0/SQ1zaZSV5fW8uknN7H6a4v66bRIERG5UKEkT8c4v8apPXP8/aQbhZo8bSnaHNA2MnHSBUYjItI1D9wygbv+uI7iqgaiIlzcODmbD80bypTcJIwxlIS4j11pXSmldaWd9nMZF9PT5hLTMJcNOwezZMvZxCe4xAlg6c5TPP92YAGes+qbfGw9Vs6CMRlBX1NERMJHKMnTP4H/Z4z5uLX2/1ofNMZ8EWfK3kMXGpy0FGryVNiwK+Bf+oq82d0QkYhI8CZkJ7H6a4s4VFzNkEGxvbb+aljicEZEL6SgYCxv7PT5W0MrvuP1dZ5oNQY5iiUiIuEnlOTpB8ANwK+NMZ/GX4bAGPMnYAYwHtgP/Hc3xSh+RSF8K7v1VAE2ouU3sta6uHlc58UiRES6m8ftYnRmG4uJull0RDQzUq+ksXw2qzbFsq3RB/g6PQ9gypBk3j0jlyf3L+NoaDmWiIgMUF1Onqy1lcaYS4EfAh8Czm4k8mGgHvgL8CVrbXl3BSmOUEaent+9KqAtypdLZnzfbIgpInIhOtuce8ygceS4F7Blz3Be3nY2Weo8aUqLj+S26bncMSP3XHL3zwJXJwNUPtyxB/EkbcIdcwTrjaX+9PXAzKCei4iIhJ9Q9nkaZq0tAD5tjLkfGAMkA1XAHmttaHPLpFOhJE/rT70T0JYXO6E7whER6XXZ8dnMHTyXNSfXnGuL98QzK30R1cUzeXtdFBuaghtlinAZrhybwbtnDmHBmHQ87pabhqfERXKgjbddT1Qxw4fvoiZyHWcai1ocixnyJ2qbrgVaVtxr8vo4VVFHSlwksZEh7U8vIiL9QCjv4G8YY9Zba99jrfUCOzs9oxPGmHuBLwO5wGbgi9bawBJx5/un46ypuhFwAW8BX7DWHrjQWPqzUJKnIzU7AvZ3mpM9o5siEhHpfT9b8DP+sfcfHCw/Sl1VDu/sGsJzWxv9RztPmsZkJvDumbncOi2HtPiodvvlp8exvlUdiuTs5dT5nuckQGPgOcZdx9HqPcAIrLVsPXaG/7xznOe3nKCkuoH4qAj+5/bJ3DB5cLBPV0RE+pFQkqcsOi9VHjRjzF3Ab4EHgfXAZ4FXjDFTrLWH2ujvAZYC0cC9OJMqfgC8aIyZZK0NbSfZfs7arq95OlVZRoPrGK13Vrll7KXdF5iISC+y1rK/sIldu6fz3JYs6hp9tJnFtJIYHcEtU3N498xcJuUkBbWZbVv7UtX5Ot8/6mRlBb9cto//bD7OwaLqFseq6pv4f//cwlXjM4mMOD/SVdfoZfmeIvYVVjIhJ5GFYzK04a6ISD8USvL0FrDYGBN1oVP0jPOX4QHgUWvtA/62pcAe4AvA/W2c9mFgNDDWWnvEf04B8CIwCQh+O/kwUtPg7XIFp2d3rcaYlue4mlIZl5HbnaGJiPS4qvomnt18nL+uOcLOkxVBn3fpyFTunJXH1eMzifb0TnW/p9YdxVvVflGMmgYvh4qrGZkRz+oDJTy7+Tgvbz9FZX3TuT73LxrFF68a3RvhiohIF4SSPP0O+CWw1xjzEnAIqG2ro7X2F51cayQwFHiu2TmNxpglwLXtnPMu4OWziZP/nM1AdrBPIBxV1HX+zWprbx9dH9CWGTWuO8IREekV9Y1evv6fbTz7znGqG4IrfTco1sO7Zw7hfbPzGJ4W18MRhuYXy/axvqCU0+1Mx35s5SE+t2gUbpdGn0RE+pNQkqenm/18Xwf9LNBZ8nT2a7X9rdoPAiOMMW7/uqrmJgNPGGO+A3wSGAS8BnyyeUI10FTWNXXeqZX9FducFWHNTE2f1k0RiYj0vOoGL0+uDe6tffawFD4wN49rJmR1yyhTflJ+u8fGDBrDTSNu4v82PEclezu9lvGU4I4+ga8xBV9dDku2neywf2VdE9UNTSRGe7oUc0VdI2/uKaKwoo6FYzMYkR7f+UkiIhK0UJKnj3bj4yf671tPIq/E+dgfB7Sen5Huj6EAuNvf53+AJcaYadbarmcZYaCitmsjTz6fj2p7OKD92pHa30lEBo7E6Ahum57LB+bkMaqb94+6ecTNPLv/WbaXbAcgNTqVG/Jv4OYRNzMmZQwAf9z4mvNVYVtcNXgSt+JJdkqZn1VffCUNRVd3W5w1DU0s23Wa57ecYPmeIhq8TtGMH720mz/fPZtLRqQFnGOtpa7R12sbFYuIDBRBJ0/GmCHANUAqcBx4yVpbcoGPf3Y+Qnt/etoqm+QBIoHrzu4lZYw5iFNs4jZajoxhjLkP/whZXl7eBYbbd7o6bW9vyQlw17Vosz4PVwxTmXIRCX/T8pJ5/+w8bpyc3WMJQKwnlieuf4LNRZuJdEUyLnUcEa6WfzYHxUZS2rImBBFxBxiUsYXGqB34CPw+LzJ1OQ0lV4AvOuTY6pu8vLmniOe3nuS1nYXUNgZOaWzyWf644tC55Km6vollu0/z4taTLN97moYmHzdOzuZ/3z2lRfEKERFpX1DJkzHmQeCrtCx6XWOM+ZK19v8u4PHP+O8TgMJm7QmA11pb1cY5VcDa5pvwWms3GGPKcQpGtEierLWPAo8CzJw5s2sVF/qRii5O21tzZHdAW6Qvkwi3vmUUkf4pJtKNy4CvnXfq+KgIbp2WzftnD2V8dmLbnbqZ2+VmRmb72zvkDorhQKvkKTJlJR1VUzLGh8tTiq8+G4/bsHBMRosRo/Y0eX2sOlDC81tO8PKOU0FN5z5UXM1zW06wZKszKlXf1PIxnttyggnZiXx8/ohOr9XdCoqrWbqzkKU7C9l1qoLcQbF8/9aJzBja8UbIIiJ9qdPkyRjzAeCbQDXwDM6o00jgZuDXxpj91tplIT7+Pv99Pi3XPeVDu5PI9+OMPLUWQfsjWGGvsovT9rYV7QtoGxSpKnsi0n/FRkZw6cg03t5X3KJ9Yk4iH5gzlJunZBMX1b82mDUBm0EEZ1JOEu+dOonrJw4mKdbDpO+80mby5PNZ1heU8vzWE7y47RSl1V3bjeNAUTX3PxW4WXpz7xwp79I1Q3V236ulOwt5decp9ha2/H5018kKPv6XDaz52iIi3BoJE5H+KZi/QvcA5cCs5pvQGmNm4pQt/zRwIcnTUeBW4FX/dT3ADcCSds55FfiCMSbbWnvCf858IB5YFWIc/V5Xy5QfLA/YIovs2PCdtigiF4dfvHcaP35lD7tOVjA+O5H3zhrC5Nzkvg6r2/34jimMSen4Pfl/XtrNsl2nOVVR12G/C9XU3lBfN2ho8rHmYAmv7jzFazs7fy7FVQ0cKKpmTFb3rl8TEekuwSRPk4CnmydOcG6q3AvAJaE+uLXWGmN+BDxijCkDVgKfAdKAnwEYY0YA6dbaNf7TfgZ8DHjJX3EvFvgJTuL0aqixDDSFdUdp/YXomNTen5YhItIVg+Ii+eFtk/o6jKDFemI7PJ4Wk0ZtUy3VjdUd9mvLX4OsMgiQFONhel4yb+wp6vLjdLfKukaW7yni1Z2FLN99usX+VcFoaOp4+qKISF8KJnlKBE63c2wvzvS9kFlrf22MiQE+h7Mx7mbgGmvtQX+XbwF34U8FrLVFxphLgYeAv+BsLf8c8Hlrrd5x/aq8JwL+dacN1oaLIiLdaVHeIl489GKLtpiIGK7Mu5Kb8m9izuA53PnCnewt67yceVfFRbq5ekIWN00ZzGUj0zlRXssbe5Z3eI7Hbbo8kyEYhRV1/ul4haw+UNwjjyEi0h8EkzxFQBvlghyNONXvLoi19iGcZKitYx8BPtKq7QDOVD9pQ1lNFT53WcBM/EuGjO2TeEREBqqrh13N95q+x3MHniMxMpEr865kUd4i4jw9szlvVISLK8dmcPOUbBaOzWixn9XQ1FhGZcSz73TLtUQpcZFcOzGLGyYNpqK2kU/+dVO3xLL/dBWv7DjFqzsL2XK0PKRruF0Gbw9OGxQR6W79a+WtdIvVR/dgTMs/RqYphUGx2ixR5KJTuBN2PQfeRpj6ftD03W5368hbuXXkrRd0jfSEqHant0W4DFeMTuemKYNZPC6ThHY2zjXG8MePzOInr+zhaFkN4wYncsOkwcwZnnKuAMOrO06FHKO1lj2Flby47RQvbz8ZUPAhGB63Yd6INK4an8lV4zK558/r2X689XaOIiL9l5KnAeidk3sC2uLd2X0QiYj0mcId8Ob/wM5nz7dt/BPctxySh/RVVNKOD84dyoMv7Dz3uzEwLz+Vm6Zkc+2ELAbFtVVkNtCQlFh+8b5p3RaXtZbtxyt4aftJXtp+ikPFXV+7lRAVwYKxGVw9PpP5Y9JJbCf56ylNXh/vHC1n2a7TvL2viNpGLwtGZ/CV68YQFaHtO0Ska4JNnqYaYz7cVjuAMeZDBJQnAGvtn0MPTUK1p/RgQFtmtD4siVwUTm1zkqZdzwceqymG3S/A3E+ebysrgJU/h72vQEQ0XPlNmHhbr4Urjo9eOoy8lFjWFZQyJCWWa8ZnkpEY+ia6F8Lns2w+Vs5L25yE6VhZbZevkZUY7Ywujc9kbn5qr2/CW17TwJt7i3h992mW7yniTKvtPg4WHSIxJoLPL9ZaYBHpmmCTp1v8t9bOJkx/aqPdAkqe+sDx6sMBbfnJw/sgEhHpVfuWwo5/d9yn0j9tq2gvrPgpbH0arPf88X/dA4OnBE7vKz0EBSsgPhNGLAR3744eDHTGGBaPz2Tx+Mw+eXyvz7KhoJSXtp/i5e2nQiqPPjoznqvHZ3HV+Ewm5SThcoW2B1YorLXsO13F67tP8/qu02w4XNruZstnvbGnKOjkyVrLoeJqlu8pYvneIjYWlOJyGe69PJ/PLBwZ8FyttVTUNuGJMMRGapKPyEASzP/RD/R4FNKtyhqOBfzLTkof1TfBiEjvaajsvE/Jfnj6Lv90vjY+XVovHFl9Pnk68Q6seNhZN3W2oOn4W+Hdf3LmlknY23y0jDn/vYziqvounzstL5nrJmZx9fgshqX1TJGM9tQ1ellzsIQ3dp9m2e7TXR4hq+mkhHptg5fVB4udhGlPEUdKawL6/HTpXiblJrFwTAaVdY2sOlDCm3uLeGtvEcfKajEGPrtwJF+4ajRG/7+IDAidJk/WWiVPYaTJ66XBVRgwh3JunirtiQjOtL3ONNbCwTdhxc/g4BuBx3c+AyUHIG1kt4cnva+4qiHovsbArGEpXDcxi2snZjE4KaYHIwtUWFHnjC7tPs2KfcXUNno7PylIzUeX3thzmrWHSoPac+qjj61n9rAUNh0pC9hw2Fr4xev7WTw+c0Bu9ixyMdJY8gCz5VQBxtVybje+aEanqmCEyMXn7AzqLnr9+1BX3nGfskNKni4Sbpdhbn4K100czNUTMslI6L21WNZadpyoYOnOQpbtLgy5Ml+EywQkNhDc6FIw1hWUdnh87cFSJU8iA4SSpwFm3fHASntRNguXq3cX64pID4tObv9YYg5c9gWoKYHlP+z6tTtLnCRsBTt1zOM2XDoyjesmZnHV+CxSgqz21x0avF6W7znNa7sKeW3n6ZDWXwEMSYlh0dhMrhybQUpcJDf+ckWL4wUl1Ux58NWgRpcuVFuJm4iEJyVPYSoqwkV9G2/4O07vC2hLicztjZBEpDflzYH4LKhqtm9PYi5c/kWY9kGIiHKm3XUkYTBEJ0HR7p6NVfqNURnt7/cXGeHiilHpXD8pi0XjMkmK6ZuiIO99dA2N3q4nG26XYcbQQSwam8GicRmMSI8/lyzuLQxcD+g8RvCP4zJ0WoRCRAY+JU9hKj0hqs3FsYcrDwW0DYkf1gsRiUivikqAj7zglBmvLYNRV8GU9zlJ01mZE9s+NzkPLv08TP0AvPI1JU8XkWFpcXxi/gh+++YBAGI8bhaOTefaiYO5cmwG8VF9/7GgK4lTcqyHBaPTuXJcJvNHpZMU270JX1p8FPNHp7NgTDqXj0rjWFltwAhWcznJMURFuDgYwn5YIhIe+v5dUrrM7TIMio1sM3kqqjsGrWbojW1dclhEBoa0UXDLI+0fz18IE94FO/7j/J46yhmZmvTu86XGY9PaP3/wVKf/hsfaLhzRXHUxbHwMjq5zHueKL0FsSpeejvSOr143lvfNHkJZTSOjM+PDrpT22KwEFo7NYNHYDKblDcIdREn0GE9wm+G6DEzPG8SCMeksGJPB+MGJLcqQJ8V4uGveUB5f7WwJEu1xMTc/lStGpTN/TDr5aXH86OXd/N+bgfstisjAEF7vmAJAalxku/tnVNuTAW0zc8b0dEgi0h+5I5yS4gu+DlgnqWm9/nHCrbDql9DU7MuY4fOdNVP5C5zyaps62LKv9CCs/hW889fz19j3KpzYBB97uXufj3SboalxDE3t6ygg0t35etwIl2HeiFQWj8tk0bgMcgfFdvlxcgfFMDwtjkNtjAidHV1aODady0d2PHpljOGBWyZy92X5VDc0MTwtjuggEzMRGRiUPIWh9ISoNttPV5WDu2UlImtdzM7RDuoiF7X0Dt4DMifAR1+Ed56AyFhnpCpnRufXPLnZOaf5/k/NHVntbMibkBVy2DLwzc1PZdOR8oD2xOgIFo7NYPG4TOaPSScx+sKm4xlj+P1dM/nyP7dyoryWnOSYdkeXgpGX2vUETkQGBiVPYSg9IYqymsaA9k0n9wa0RXjTiItqO9kSEQEgZ7pz64rXv995n9pyJU/SofsXjaK4qp5XdhSSGhfJ/DHpXDU+k1nDUvAEMSrVFSPS4/nXJy/p1muKyMVHyVMYSo9vO3naXXIgoC0hQvs7iYhI/xTtcfPjO6bw4zv6OhIRkeBo858w1N60vaNVBQFtg2PzejgaEREREZGLg5KnMNRe8lTacCygbWRyfk+HIyIDXifrQeKzIDKhd0IRERHpQ5q2F4baS54aXIW0rvkzOXNUzwckIgNb5njYvzSwPX0sXPJZp/T5by+H4j29H5tImKmoa2T1gRLe2lvE3sJKkmIi+dr1YxmRHriBcWl1A8fKakiLjyI7OaYPohWR1pQ8haH0+LaSJy+uyJKA1rm5Y3s+IBEZ2OZ9Fva8fD45GnopXPo5GHlVYOlzEQlQUFzNL5ft4619RWw6Uo7X13Ij4HWHSlj3jcUYAxsPl/H2vmJW7Ctm+4kzWOuUa//WjeO565JhffMEROQcJU9hqK2RJxNZijHelo3eeIalZPRSVCIyYMWnw6dWw4nNzs/JWksp0hV/33C0w+MVdU2M//bLREW4qW30Bhxv8ll++NIu3jNzCDGR2ldKpC/pK8Mw1Fby5IosCmiLYXBvhCMiFwOXG3JnKHES6SE+S5uJ01l1jT52n6po97iI9A6NPIWZaI+L+KjAfzZ3G8lTenRub4QkItI5a+HwSlj3Ozi1FbKnwXU/gbjUvo5MJGzYzruISA9T8hRm0hOiMCaw8pWJCkyehiYM742QRETa522EHc/A6kfg5Obz7aUHnU10P/TvPgqsfymrK2Pp4aXsK9vHuNRx3DziZiJc+hMdjoYMiu3rEESkB+mdOcy0XSyi7ZGn8ekjezocEZH2bfgD7H4RKgK3UQDg0JtOcuX29G5c/USDt4G3jr3Fcwee4+3jb9Pkazp3bHfpbr4+5+t9GJ2E6uoJmTzy+n5OVdSda4vxuJk3IpUrRqVx+eh0NhaU8eV/bW3z/EGxHi4dmcaGgrIW1xCR/kHJU5hpu0y5xRV1OqB1VrYq7YlIH1r3aMfHfU0XbfL0yOZH2FS4iYqGttewPLP/Gb4868safQpDGQnRLLn/Mv616RiNXsu0vGRmDB1EVMT5Qg+5g2LYfuIMf1vnFJKYMXQQl49O4/KR6UzITsTlMtz6q5VKnkT6Ib0rh5k2K+25qzHu2hZt1hfBtGxN2xMR6Y+WH13e4fHaplpqm2pJ0ObDYSk1Por7rhjR7vGoCDcP3jKRB2+ZiLW2zen4ItI/qdpemEmPjw5oa6vSnseXQWSEcmMRGcCa6vs6ApELpsRJJLzo03WYabNMeRvFIpI92b0RjoiIwx3ZwUEDY2+AvS87U/UuRPlRWP872PI3qC6CUdfA7b+HqPgLu+4AUFZXxqsFr/Lakdcoqiniyrwr+fTUT+N2tdwXqLqxmrUn11LZUMn83PkkRyf3TcAiImFIyVOYaXuPp8D1TtlxQ3sjHBERR94cKNzWsi0iBqZ9AOZ+ClJHwPezQkuerIUjq2HNb2D3C2B954/tfQmW/xCu+cGFxd+D0mLS2Fu2t81j0e5oFg1dxNKCpTT4Grp87dqmWt448gZLDi1h1fFVNNnz/30PbDtAdEQ0902+j7qmOt4+/jYvHXqJt469Rb3XGbVLjkrmr9f/lbxE7d8lIhIMJU9hJtiRp9GD2p9rLSLS7RZ9B84ch/1LISkXpn0QZt4NsSmhX7OxDrb/C9b+1tkbqj1H14b+GL3gPaPfw6oTq879bjDMzprNTSNuYvHQxcR54njz6JtBJ09NvibWnFzDkoNLWHZkGbVNte32/eU7v+TQmUO8fuR1appqAo6X15fztz1/48uzvtz1JyYichFS8hRm0uIDp8a0teZpatbo3ghHRMQRnQjv/xt4m8B9gX9aKk46Zc43PAY1xZ339zZe2OP1sEVDF/HYNY/x5rE3yYjNYHHeYgbHD+7SNay1bCvexpKDS3i54GVK60qDPveFgy90ePxo5dEuxSIicjFT8hRm0lrv82QaMZ6ygH6X5I3rpYhERJq5kMTp2AZnat7OZy58bVQ/MzNrJjOzZnb5vIIzBSw5tIQXD77IkcojPRCZiIh0hZKnMJIYHUG0p+XCX1dkCcbYFm3Gm0x6fGJvhiYiEhpvA2xd4kzNO76hr6PpVz768kfZU7anr8MQEZFmVKo8jARbLCLOdG06iIhIn3lkJvz7nuASp9xZcPmXej6mfiLYxCnBk8Bto27jfy7/n077TUmf0h2hiYhctDTyFEbaTp4C1ztlRA/pjXBERC5cdeB7WAsuD0x4F8z5BOTOgOOb4O3/7Z3Y+jGPy8P83PnckH8Dl+deTpTb+fuw9PBSXjvy2rl+MRExLBiygOuGXcelOZey4vgKPvfG5/oqbOlmXp9l+/EznK6sZ2hqLKMztamySE9T8hRG0hPa2CC3jUp7w5PzeyMcEZGeE5sGMz8Gs+6GhKyun99UD4dXQm0ZjFwM0UndH2MvMxhmZc3ihvwbWDx0MYmRgdOzf3j5D7n04KUcrzrOmJQxXJFzBbGe2D6IVnrKkZIa3t5fxIp9xaw6UMKZWqdgittlePCWCXxgjrYqEelJSp7CSHrrYhG0PfI0KX1kb4QjItL9sibD3E/ChNvAE/iFUafKj8KGP8KmP5+v1JcwGO5+FZL7715GQxKGsKt0V5vHxqaM5YbhN3Dd8OvIjMvs8DrREdHcMfqOnghR+oF7Ht9AaXXbJe29PsvDr+3jfbPycLlML0cmcvFQ8hRGAqft2TZHnmbnju2dgEREuiIhC8oOBbYbN4y7EeZ8EvLmguniBz/rg/3LYP0fnE1zm2+iC1B50kmoFn835NB72memfYb7X78fr/UCkBOfw/XDr+eG/BsYkax9+8TRXuJ0VlFlPcXV9WS0MVNFRLqHkqcw0jp5MhEVGFfLN1Lri2JChtY8iUg/NOcT8PJXzv8eMwim3wWz7oHkC3jfOrUVnrit4z4lB0K/fi+4IvcKXrrtJd45/Q5DEoYwMW0ipqtJpIiI9DglT2GkdfLUVqW9KJuJy6UiiiLSD839BKSOdNYipY+FcTdBpNbjnDU4fnCXN8+VgSum1dYkItI/KHkKI63XPLU1ZS/Fk9tb4YiIdN2oxc5NRDq0YEw6qw+WBLS7DEwZksxlI9P408oCKusH1obSIv2dkqcwEjjyFJg85caryo6IDGDBTGWLiIamup6PRaQHfeyy4ZRUN/DqjlNEuF3MzU/hspHpzBuRSlKMB4Cn1h1V8iTSy5Q8hQljICUuskVbWyNPY1K1sFhEBrDUUeCKAF8bHxjzFzjrp5rq4V9393poIt3J43bx9evH8fXrx/V1KCLSjBbHhImE6AjcrUqPtrXmacbgMb0VkohI74uKh2t/5FToA4hKcqr0fWYDfPhZZx2VS98LiohIz+gXf2GMMfcCXwZygc3AF621qzvo/zxwYxuHEqy1VT0SZB9LjPa0+N1HHS5PRYs2aw2zh4zuzbBERHrf7HthwrvgzDFIHwOemL6OSKTfavL62H6igsq6RvLT48lJ1v8vIheiz5MnY8xdwG+BB4H1wGeBV4wxU6y1bWwIAsAU4OfA31q11/RYoH0sMaZl8tToKgzo4/amkhStylUichGIS3NuItKShQNFVazcX8yKfcWsPlhCZZ0zzTUu0s0j75/OwrEZfRykSPjq0+TJOJtYPAA8aq19wN+2FNgDfAG4v41zkoEhwMvW2jW9F23fSoxu+U/V4DoV0CfBnd1b4YiIyEXAWqv9psLM9b94m+KqtjfTrW7w8ovX9yl5ErkAfT3yNBIYCjx3tsFa22iMWQJc2845k/33W3s4tn4lmJGnrJi83gpHREQGoAZvAyuPr+SVw6+w+sRqGn2NfGj8h/jE5E+cS6IqGyp589ibvFrwKhsLN+JxefjMtM9wx+g7+jh6AdpNnM7acbyiw+Mi0rG+Tp7OLtDZ36r9IDDCGOO21npbHZsM1APfN8bcAsQAS4DPWmsDh2MGiITo1slT4FPNTx7eW+GIiMgA0ehr5K1jb/FKwSu8fuR1qhpbLh3+9eZfkxufi8WytGApK0+spNHX2KLPA6sfYGLaRMamjO3N0EVEel1fJ0+J/vvKVu2VOJUA44DWX5FMBqL8fd4F5APfB143xkyz1tY372yMuQ+4DyAvLzxGZtwuA61SxuTYlsmT1xVYpnxSxqieDEtERAaglcdXsvL4yg77fH3F1zu9zqbCTUqeellspLuvQxC56PR1qfKzE6ltO8d9bbT9FLjSWvs5a+1b1to/AbcD44D3tO5srX3UWjvTWjszPT29O2Luca03wwUYPzixxe8uT2BRwfn5I3ssJhGRAafiBGz6M6z/PVQX99zjnDkGb/0E/vYBeOUbUF3Sc4/Vh1qPRknPWzCm7c81Hrdh9vAUPnulPheIdLe+Hnk6479PAJov4kkAvG2VHbfW7gZ2t2pba4wpx6nC95eeCbX3ZCfFsL28ZVuU53ye67M+6n2Bc5Yz4sIjORQR6TM+Hxx8HTY8BntegrMzw1c8DPcsg4TM7nmcxjrYswTeeQIOvEGL7wiPrYe7X+2ex5GL2levG0tdo5f1BWXEeNxcMiKVS0elMXtYCnFREdQ3efnl661XRojIhejr5Gmf/z6fluue8oG9bZ1gjHkvcMJa+1azNoMzla8HvzrsP8rry/G2WgqW4EkgOiK6jyISEenn6sphxc9g45+grCDw+JmjsOM/MPcToT+GtXByi5MwbfuH85htOboWyo9C8pDQHytEUe7AmQ0SvmIjI/jxHVP6OgyRi0p/SJ6OArcCrwIYYzzADThFINrySSDRGDPDWnt2Wt/1OIUj3mrnnAGluDYwR0yNSe2DSEREwsSht5xbR84cDe3aNaWw9WknaSrcFtw5deU4u270rolpE4mNiKWmqeW2iDERMSwYsoBrhl3D0ISh3PnCnTT4WlZtS49JZ/HQxVw99GpeO/Iaf931194MXUSkX+jT5Mlaa40xPwIeMcaUASuBzwBpwM8AjDEjgPRmezr9N/AS8IQx5jGcin3fA/5lrV3V28+hL7SVPKXFaLNIEZFe4/PCgdfhnb840/+8HZeH7i+SopL4xZW/4KEND1FeX86ktElcO/xaLsu5jJiImHP9Hl74ML/d+lsavA3MyJzB1UOvZmrGVFzGmUL++tHX++opiIj0qb4eecJa+2tjTAzwOZyNcTcD11hrD/q7fAu4C39xCWvtK8aYm4FvA8/grJv6o7/fRaGkNnCxsUaeRER6QckBZ4Rpy9+g8kRfRxOSOYPn8PRNT3fY5/Lcy7k89/JeikhEJHz0efIEYK19CHionWMfAT7Squ0F4IUeD6yf0siTiEgH3J7O+8RnQVWQWwPWV8HOZ52k6UgXJjhExMD4W+DQm1B5MvjzRESk3+oXyZN0jZInEZEOZE8H4z5fSe+s2DSY/iGYfhfsfgFe/Wb717AWjm+CTX+C7f+GhsDtIdqVOwumfgAm3gbRSfCbS5U8daCqoYpDZw6RGZdJRmxGwPFGbyMbCjdQUFHA6EGjmZ4xHadOlIhI71PyFIbaLBgRrWl7IiIAJA6GW38Dr34Dassgbx7M/CiMvREiOqk2V1vuVMrb+Cco3B78Y8ZlwJQ7YeoHIUMbxXamtK6U5UeX89rh11hzcg2Nvkbcxs2XZ32Z9497P1UNVaw4sYLXj7zOimMrqGysPHfu/dPu597J9/Zd8CJyUVPyFIbaWvOkkScRkWam3AkTb3d+dgf5p27HM7D+D9BUG1x/44bR18K0D8Koq4KbLngRO1V9imVHlrHsyDI2Fm7Ed65grsNrvfxw3Q95peAVthVva3fT3T9s/wMfm/gx3C53i/aaxhqKa4vJjs8mwqWPNz3BWqtRP7no6d0lDGnanohIEIJNms6qOBZcv/SxTsI0+U6ID5xmJucVnCngtSOv8fqR19lWHFwZ902nN3V4vLqxmoqGCgZFD+Jk1UmWH1vO8qPLWXdqHU2+JjJjM/n14l8zetDobngGA1uj18eWo+Ws3F/C2kMl1Df5uHxUGp+9chRul6HJ62Pb8TOsOlDCmoMlrC8oxVq4aUo23791ItEed+cPIjLAKHkKQ8V1Sp5ERHpVVKIzkjXtg5AzA/Tte5ustewp28Nrh19j2ZFl7C/f3yOP87ttv2P9qfXsLt0dcKywppAfrPkBj1/3eI88drjzWsvv3jrIygPFrDtUSk1Dy7WBGw+X8djKAmYMHcS6Q6VU1TcFXOOfG48xbnAid182vLfCFuk3lDyFmQZvA2fqz7RoMxgGRQ/qo4hERAaw3Nkw4yMw4VaIjOvraPq1lw+9zFO7n+J41fEef6y/7PxLh8f3lO3p8RjClddn+cGLuzrsc6a2kdd3n+6wz6r9xUqe5KKk5CnMlNaVBrQNih6k+d0iIl2RmNP+sehkmPI+mP5hyBzfayGFu+0lXSiwAeTG53Ksqv2pkukx6SwYsoAXDr5AbbDr0Pxar6eS7tfg1X9juTjpE3eY0XonEZFuMHIxpI+DombfwA+9zBllGncTeKL7LLSBbGTySBYPXczivMWMHjSak9Un+ejLH+VEtbPh8IikESzMW8iVQ65kQtoEXMbF0sNLu5w8tdbobWTT6U2sPL6SkroSrsi9gmuGXdMdT0lELjJKnsKMkicRkW4QnQgfexl2/Bt8XshfCGkj+yYWnxcK3oat/4DTO2HwZFj0HYhN6Zt4utmktEksylvEorxFDEsa1uJYdnw2L9z2AntK9zAoehA58YEjgllxWZTXl7d7/ZHJI9tcW1VaV8qK4yt48+ibrDqxiqrG83t1PXfgOWoaa3jXqHeF/LzCQaTbRX5aHAeLq9vtkxAdQWVd4Lqmc8ejIshLjWXHiYpOH6+u0cvuU5VEuAxjsxKIcLtCilukP1PyFGaUPImIdJOYZJj5sb57/NO7YevfYOvTUNFsndCJTVBxEj7wdN/F1om8hLx2j7mMixmZM84lTFlxWR1ey+PyMDFtYrvHPzbxY3z5rS+f+z3CFcHsrNksGLKABbkLSIpKYs6Tc1qcU9tUy4K/L8Bi273uM/ufGfDJkzGG+xeN4v/9cwuNXue/RYzHzazhKVwyIpVLR6QxPjuRFfuLuf+pdzhT20hspJtZw1KYNyKVS0akMiE7iZX7i/nwH9cFXL++ycs7R8pZc9CpxrfpSDkNTc50vrn5KTz64ZkkRquEvwwsSp7CTJsb5MZog1wRkbDy9w9B2aH2jx9YBo11/Xb64A35N/D3PX8/N+LjcXmYlz2PxXmLmT9kPinR3Tdqdt3w6xiWOIz1p9YzOH4w8wbPIz4y/tzxmsaaNs/rKHECAoovDVS3TsthYk4iB4qqGRQbydQhyURGtBwRmj86nXXfWERlXRNJMR48QYwYbSgoY/J3X6W+qe21T2sOlvLi1pO8d3b7ibZIOFLyFGba3CA3WiNPIiJhpaPECcDXBN76fps8JUQm8PSNT7PqxCpcxsW0jGktEpruNi51HONSx/XY9Qe6kRkJjMxI6LBPVISbqPjg922qbfR22mfLsXIlTzLgKHkKMyV1bSRPmrYnIiK9zOP2MH/I/L4OgwhXBC7jUoW9fsg2G/yrrm9iw+Ey1h4sYefJCmIj3XxqwUgm5iT1XYAiIVDyFGa05klERMKCzwfHNzq32BQYd3PLkbSaUtj7MuxeAqe2QXIeXPsjyGp//VNbIt2RTM+YzobCDQHHhiQMYX7ufIYnDed7a753oc/oohUXFdrHxUPF1fzwpV2sOVjK9uNn8PpaTqVctus0b3xpAdnJMd0RpkivUPIUZpQ8iYiEmZThUNjOHkiDpzp7Si17ANpZuxNWfF44shp2Pge7nofKE+ePDf8L3PyIP2F6AQpWgm029av8MDx5J3x+K7iCnz4G8MPLf8gP1v6AnSU7yUvIY37ufOYPmc+wxGEYY9hfFliNT4I3JTeJtPgoiqvqA46lxUcyJz8Vn8/y0vZTLY6tPVTK2kOB+1OeVd/kY9nu03xo7tBuj1mkpyh5CjMqGCEiEmYu/y/Y/zo0+stFJ2TD5PfAlPdChn8dzxs/6Lv4LpS3EQ69Bbuec0aRqova7nfoLfj55I6vVXEMivdBxtguhZAVl8Uvr/xll86R4EW4XTz98bk88PxOTlfWMzwtlnn5qczNT2VkRjzGGJ5adyQgeQrGmZqGHohYpOcoeQojNY01ARsFelweEiMT+ygiERHpVPY0+Pw2OPQmJAyGIbO7PLLSJ+oroakB4tr4gq6xDg6+4Yww7XkR6sq773EvcENc6Rn56fE8/rHZfR2GSJ9T8hRG2ht1Msb0QTQiIhK0uFSYeFtfR9G5M8dh57Ow4z9wbB1gnGmFtzwCTXWw/zUnYdr7CjRU9nW00o9MyO78i9xIt4sGrwp7SHhT8hRG2lzvpDLlIiJyISpPnU+YjqxuddDCliedW0SMRoWkXZNykrhpSjbPbzm/zi0vJZY5w1OYm5/KnPwUnlp3hF+9caAPoxS5cEqewoiKRYiISLeoOu1PmJ6Bwyuhkw1lgeATJ3cUjFzk/LznxcDjGRNg3I0w9kZ49tNwamvH16spdUa8ivc5lfjG3hge0x4vMsYYfvHeqXz00mGcqW1kTGaCqujJgKTkKYyoWISIiISsusQp6rDj31CwArpzXyRPLIy6Gsbf7NxH+Tdk3fAYbP4rRMY7CdXYGyAl//x57U07LyuA3S86ydfhVS2r8l3yWbj6+90Xu3QbYwzT8wZd8HXqm7wcL6slKcZDanxUN0Qm0n2UPIURjTyJiFykSg/Btn86U+vKCmDYZXDrb9ou5tBcTalTFnz7v51qd82TkAsVlQhjrnP2bxq5CDxtjDLM/Khz64q/fwjOHG3/+Po/wKLvgNvTtetKv1XT0MSmw+WsKyhl3aES3jlSTn2TD2Pgi4tH89lFo/o6RJFzlDyFkZK6koA2JU8iIgNUTQlsfdq5HVvX8ti+V2DJF+E9jweeV1vulAzf8R+nIp6vqftiikmBsdfDuFsgfz5E9MCoQEeJEzj7YdVVdJ44hrOyw3DgdTiwzEmWE7Lh+p/AoIG3H9IfVxbw8Gv7aPIFTh21Fh5aupdbp+UwJCW2D6ITCaTkKYxo5ElE5CLyyxkdT607vPL8zw3VsOcl2P4vZ32Qt4t756SMcKoBTngXZIyHF74AGx9zjsVnOuuMxt8MQy8Dtz46dLv6Smcq5f5lTtJU2qqowqlt8Oc9cP/m9qc6hqnS6s5fq5uOlCl5kn5D74BhRMmTiMhFpLM1SY11TsK07Z/O/dlNeIOVPNSfMN0GWZNafii/6WG47AvOBrgpw3uuQIPpH4UfrLXsK9/HW8feYsOpDTTZJm4ZcQs3jbipZx7Q54OTm/2jS6/D0bWdjxCWFUDRni5vIDwQ+GwQBU1EeomSpzCighEiInJOQyU89d6unZM0BCbc6iRM2dM6HsXojSli2VPhxKa2jw2eAmNugFW/gIaq9q/hbXRKrO97FcqPwtBLYNY9nSZ8jb5G3jjyBm8df4u3j71NYU1hi+NrT64lyh3F1cOu7uKTaseZ4840ygOvw4E3oLa069eoD++9teKjtE5Nwp+SpzBhraW0LvCNNjVayZOISNgzrp67dkK2Mx1v4m2QM6N/Tfta+E04uRWObwCXB4ZfDmOudwpRJOU6fdb+NvC82jInCdn7Eux7DerPnD+28xln76rF3+nwoY9UHuH+N+7vsM+yI8tCT54aapxKgWdHl4p2hXadAWTh2HR+/Mpu2hpIGpwUzZzhKewprGLXyYreD04kSEqewkRFfQVNrYb04zxxxHo0B1hEJOzlTHc+YLeWmAMTb4fR18Cfbgj+evGZMP5WJ2HKnQ2uHkzOLkRcKtzzmrPvVHRi2xX72vLITDrcm2rr050mT8GoaOjCh3hroXD7+WTp8Grw1of4yMYZOWtvKp+1ULIfDi53blWFkDoKrv1viLnwUuE9ZWxWIn/8yCx+/cZ+ahq8TMxOYvbwFGYPTyF3UAzGGD7/t3eUPEm/puQpTBTXab2TiMiAdcNPnSl4RbshKgkm3AKT3gNDL3USn8ZaZ3Sqo3VQ0clOUYeJdzilzMNlI1ljICGziyd1sgamtizkcLqk6rQzBe9swlR9GnCiK3W5iDSGhGDX6yTmwIiFMGIR5C+Av77bGZFrbs+LsOGPcOhNqDje8tix9VB5Ej78zIU+qx61cEwGC8dk9HUYIiFT8hQm2lzvpCl7IiIDQ8pw+NQapzx5TErgSJEnxkmkCt5u1R7rTHObdIfzoTsisvdiDiMpMSkYDLazpKsTjfXVbN75FJsPLcWUHuCmkwfJ9Dp7Z51xGdbExrAqNoaVMdEURjgfse4pP8Nny84QMPYXEeMkuSMXwYgrIW1051MqV/y04+OH3nLWRZ3dpPgi5vVZdp+qoKiynqGpcQxPi+vrkGSAUPIUJlRpT0RkgDMG4jp4X7/9D/DK1+DkFkgf66xjGnMdRA7wD4WeGKi9sEukRKdwWc5lvH28ZfKZHpPO5bmXc3nmLBpPbePL+59seaK1nDyxgbe3P8HKU+tZ6z1Dtcuf4Hjg53k53FpZxSGPh21RkfjaSH5+n5zEldW1TGpocKoajrjSSXTz5nb/PlnW66y1ugiTp9oGL5uPlrOhoJT1h8vYdLiMqnpn2mOEy/D9Wyfy3tl5fRylDARKnsJESa02yBURuaglZMIdf+zrKHpf/kLY/ERge1IejLkWhl8Bf/9gp5d5eOHDPLb9MU5WnyQ7Ppsr4oczpnA/Zv+rsOzXvBXpgqyW08lWH1/B1Sea7aflCkyOnkmI7/SxN876IJPmfQ3iz1/fWkttYw1R7ijc4TLFsh8pqapnw+EyJ1kqKGP78TNtbrQL0OSz/O+re3j3zCG42/g3FOkKJU9hQiNPIiJyUbruR4B1pqQl5sDoq2H0dZAxzhmtq++gjHkzkdby8fhRcLIANvw2cCNaogPO8XZTZUJv1kSIz6C8rpw1p9aw5sQaVp1Yxcnqk0S5o/jGnG/wrlHvanlS+tjANU/NuTzOSFNn+4ENANZCQXE16wtK2VBQxvrDpRws6tq+ZsVVDRRW1JGdHGRREpF2KHkKE9rjSURELkpRCXDrr0M798wx2LfUuR1c3vWNhLvJ60df57XDr7GjZEfAuqt6bz3fXf1d5g+ZT0p0yvkDs+6GPUtaFr/InAT5853RuKHz4OdTzxWpGMi+9u9t1DcN/CRRwoOSpzDR6GsMaNPIk4iISBua6uA3lzqlw4MU186Ur67Ijc8lwhVBQUVBi/atRVs7PM9nfWwq3MTioYvPN+ZMh0+shKNrwLidgiHx6RccYzhS4iT9iZKnMKaRJxERkTZYb5cSJ4DJ7niSvV7K3YHrj8b73FyaPIbLRtzIyzWHeWrP3wGIiYhhTtYcLsm5hEuzLyUvMY+fbvwpj21/rMsht97LEYCkHEi6vcvXulhlJUYza3gKs4YN4mdL91JWE/jFs8iFUvIUxtKiNfIkIiISGgO5M2HUNTDqKjxZk3n88Bv86K2vUe5rYJgnictzLmPepA+Tljbm3FnTgY9OvJuaphqGJAwh0t2yPLwrsCi5dMGguODL7Y/JTGDmsEHMGpbCzGGDyEl2NtoF+M3yA4CSJ+l+Sp7CWEpMSuedREREBrKuVKqLToaRi2HU1c59XMsZHPnDF/Ho8HWdXmZw/OB2j01On9zhucMSh1HbVEthTWFQIV9srp80mMdXFdB6FmWk28WUIUnMHOaMLE3PG0RyrPY1k96n5ClMDYoahMfl6eswRERE+pYnBnJmwPGNbR/PmuQkS6OuhpyZ4O7Zjz6X5VzGdcOu46WClwBIjExk7uC5zMuex7zseeTE5/ClN7/EKwWv9Ggc4WrWsBQe/9hsnt5wjMYmH1OGJDNr2CAm5iQR7VFJd+l7Sp7ClNY7iYiI+F3/v/CPu6D8CETGQ/4Cf8J0FSRm92ooke5I/ueK/+Gz0z9Lk6+JvIS8kPdxqm2qpaimiKSoJJKikro50v7r8lHpXD7q4iyOIf2fkqcwpUp7IiIifjnT4bPvQGMNRERDRN9O5zLGMCRhSJfP8/q87CzZyZqTa1hzcg3vnH6HRl8jUe4ovj3v29w84uaAc0663RS73WR5m0j3qiqdSE9T8hSmlDyJiIg0444Ad2JfRxGyR7c9yoOrH6SysTLgWL23nh+s+QHXDLuG6sZq1p1ax9qTa1mXEsmRiBwAon0+vl9UwjU9FJ+1luNVxwHIjMvU0gG5aCl5ClNKnkRERAaOfWX7Ojxe01TDzCdmtmyMOF/Zr87l4qHUQVxtLaYLj+uzPvaV7WND4QYavY3MyprFhLQJWGs5dOYQGwo3sP7UejYUbqC4thiAqelTeXjhw1pCIBclJU9hSsmTiIiINHcyIoKS+nLSyGq3j7WW/eX7WXdqHRtObWBD4QbK68tb9Il0RRIfGU9pXWmb19hctJmn9z7NJ6d8sjvDFwkL/SJ5MsbcC3wZyAU2A1+01q4O8tzvAN+11nbli5awp297REREwlOEqyc/frWs8W2t5VDFIdafXO8kTIUb2k2KzmrwNXTaZ8vpLRccqUg46vPkyRhzF/Bb4EFgPfBZ4BVjzBRr7aFOzp0IfL3no+x/NPIkIiISnuZkzWHJwSVtHovzxDErcxbLjy0P6drWWg5XHGbdqXWsP7me9YXrz023604+q+IUcnHq0+TJONtAPwA8aq19wN+2FNgDfAG4v4Nz3cAfgSIgp+ej7V/SopU8iYiIhKMb8m9ge/F2/rXvXxgMk9MnMzd7LvMGz2NC2gQ8Lg+/3/Z7fr7p522ePyJpBLMHz+bZnU9S42o58eaO1z9OaatpeANJWV0Zm09v5p3T73C86jiZcZl8csonSYhM6OvQ5CLR1yNPI4GhwHNnG6y1jcaYJcC1nZz7BSAB+CXwox6LsJ/SyJOIiEh4inRH8q153+Kbc7+Jz/ra3AfqrvF3UVxbzBtH3sDtcjMjcwZzBs9hTtYc0mOdPZBe3fEkNa3O687EKdIVyeT0yaREp/Dq4Ve77brBstZytPIom05vYvPpzWw6vYlDZwInJW04tYG/3/h3nO/kRXpWXydPo/33+1u1HwRGGGPc1lpv65OMMSNxRqyuAWa2Pj7QRbgiSIwK33KsIiIi4uwH5TZtb6DrcXv46uyv8tXZX+32x43y+Zha38Csujpm19aR5vXx36mDOBYRQabXywwTy6xb/sCk9ElEuaNYdXxVp8lTYXUhGws3sr1kO5GuSG4eeTP5SfldiqvR18jukt28c/qdc7eSupJOz9tVuouCigKGJw0H4HTNaZqiNxMVtR8TUYmvIZ2G4gVdikWkPX2dPJ3NAFpvalAJuIA4oKL5Af9Uv98Df7bWrjDGdJg8GWPuA+4DyMvL646Y+1xqdCou4+q8o4iIiAxYcdZSEkRhco+1TKmrZ3ZdHbPq6plcV08kgDsKMsbByS38prDo/AkpIyDL//Gq6jQcXhlwzcqGSv6z7z9sLNzIxsKNHKs61uL43/b8jSdveJL8pHwavA1sL97OptOb2Fi4kb2le4n1xPKRCR8hKy7r3MjStuJt1DbVhvTf4sldT1LRUMGWoi3OflQp0HyrZFdUIXB1SNcWaa6vk6ez/8fbdo63tRrx4zjT/QK32W6DtfZR4FGAmTNntvc4YUVT9kREROSKBh9PRAR+mRphLZPq65lV6yRMU+obiLYWXB7InQXDL4dhlzk/nzkOj8xoeYHaMnj+c3B4FRTvhZhoyMpo0WV7yXa2r9rebmzVjdXc8swtTM+Yzvbi7TT4Glo9Bnx39XdDfeoB/rbnbx0ej0jYSU1jNRDTbY8pF6e+Tp7O+O8TgMJm7QmA11pb1byzMWYI8GPgo0CNMSYCZ4QK/88+awd++ReVKRcREZHPx47Ed2YbG6OjiLaWWXV1zKqtZ2p9PbHWgisCcmbAsMudhCl3NkTGdn7h2lLY+KduiXHT6U3dcp3m3MaNN3BVR4eM8VLVVAV03xfQ1lqOltbyztEyth47g8vAVeOzmD08pdseQ/qfvk6ezm6nnU/LdU/5wN42+i/CSaz+2caxRpx1UN/txvj6JY08iYiISNRlX+Brf/sglJY5DcYNOdOdUaVhl0PeXIiM69sgL5DH5WFS2iSmZkxlesZ0pmZM5f7X7++WpMxay7GyWqrqm8gZFENitAeARq+PPacq2XSkjHeOlLP5aDkNTT6mDEnitmm57DpZweajTntJdcsRtT+uLOCxj8ziitHpFxyf9E/9IXk6CtwKvApgjPEANwBtbYDwPDCrVdv7gC/620/0VKD9SWq0Rp5EREQueiOuhE+uhFPbwBMLeXMgqoslu4OoUDessTHEALsuKSqJaenTmJY5jekZ0xmfOp5Id2SLPpPTJ3eYPLmNG6/PB6blao36Ri/rC0rZdLiMTUfK2HSknKLK+nPHp+Ul43G52Hq8nIbGJkabY0xz7eMT5gAe08SqnRO4Z9v8DuP3+iz/3nRMydMA1qfJk7XWGmN+BDxijCkDVgKfwRlT/RmAMWYEkG6tXWOtLQFalF0xxlzmv9aGXg2+D2nkSURERABIGe7cQjVoGMSlQ3VRu12yYzK4qbKa5xPOj2KdXVc1o66e6XX1ZHi93JEzuN1rpEanMj51PG8ff7tFe15C3rlRpWkZ0xiWNKzTolgfGPcBVp9YzZ6yPQAkRCYwJX0KU9OnMi1jGhPTJjL3L9dg3eUtznv/79ZgGuMZZ44w3bWPb7n2Mj1qHwnUcNym85fji8kxxXzO7GdK1AHiTV2L829zryCOOh73XtNhfKcq6jo8LuGtr0eesNb+2hgTA3wOZ++mzcA11tqD/i7fAu6CIMrJXCSUPImIiEi3cLnhhofgP5+ExmowLsia7Ez9G3oJ5M2Dk1v4wV9u5drqak5HuBna2MSks0Uomvl2cQk/ThlEnctFXmMj0+OGMH3mp5ieOZ28hDyMMTT5mthatJXaplrGpIwJ6TNNVlwWT97w5LkKf8MSO0+4AB6JeJgFrgJiTEPAsSRzmB+6/tDpNW51r+w0eZKBrc+TJwBr7UPAQ+0c+wjwkQ7OfRh4uAfC6reUPImIiEi3GX8LDJ/vjD7FZ0B0Usvj2VMxrgiuqG1vRMVAzCDeXVnKHZXVNOIvEz79Khj1rhY9I1wRTM+cfsEhR7ojA/eR8nnh9E44uo5EW3muKtlZ01wHiOlioYnWUltd1RiwQdRyPl1Zx84TFURFuJmWl0y0p+39vaT/6xfJk3SNkicRERHpVjHJzq3NY4Pgmh/Cy18F63Wq+A2e6oxMDb3UWWu181l4/nMYWu6vBEBdBRxbB0fWOLdT25y1WQu/AVPfF3rMtWVwbAMcXQdH18LxTdDgbB0aPSSbMz3wMTfC7eK2aTlMy0tm6pBBlNU08OE/rmvRp77Jx5qDJWzxF5XYcrScE2fOJ54TshP588dmkxofRUVdI9uOnWHz0XK2HitnX2EV8dERfPbKUVw1PrPb45cLp+QpDCl5EhERkV415z6YeBtUF0PykOCq+B16G357ORRuh9Y7ydSVwzOfgKxJkDWx1bEzUHoQYlJg0FCnzeeDkn1OknR0nXMr3tMtT61dnlhorGnRlJ0UzU/vnHru91UHigNOe+dIOe99dE27l91xooIZ33+N/PQ4DhZVt9nn3j9v4NUvXMHozPMFQLw+y8GiKg4VV5MaH8X0vGRMEAU/pHspeQozMRExxHqC2KNBREREpDvFpTm3YJUd6rzPweXOKNTRtc6o1NG1ULgDaDYXbuRVzshVXeuJeCGKSYEhs8EdCbuec9qMCzLGQ+5MZ/Pg3FnOCNsvO59iGEsdE0wBXlzssUOoDnIj3vYSp7P+vLqA2cNT2XasnC3HzrDj+BmqG85PO7x5SjY/f+9UJVC9TMlTmNGok4iIiAwYr37DuXVk/9Lueaxr/hvyF0PqiPMl2r2NUFPijKS1LvNe2kbyZy2c2g7HN8CxDUw5tI5tUXtx+8uiH/Rl8eHGr3LMZlxwuE+sOcITa460e/y5LSe474p8JuYktdtHup+SpzCj5ElERET6HXdUX0fgiEpyRo/McfC2nHLHhFshLqtlm9sDCa3aOnLmCPz20nO/xkGLetD5rlN82L2U/276QFcjD8nOkxVKnnqZkqcwo+RJRERE+p3hV3R8fNDw4KbxdVXaaMid7UzDGzIb0saAywX/WAw1NZ2f3wMmmUPkpcQyZUgyU3KTGJ4Wx92Pt78daX5aHAeLO57CJ/2Hkqcwkxqd2tchiIiIiLSUlAN3/BFe/rpTDCJ9rLNHVN5c55aQBWt+41TsC5UnDnJn+JOlOc4IU2xKtz2F7jJz2CDeunthi7b/fOoSfvTSbkqrG8hPj2NybjJTcpOZlJtEUoyHF7ed5FN/3RTyY1prOV5ey/bjZ9h67AwnymvJTo7hkwtGkBDtudCnJM0oeQozGnkSERGRfmni7c6tPWOug2UPBlSww+WBwVOcJGvIHKeU+Z6XwNsA2VOdEaXc2U5BB3cvfnSNz4SIGGiq7dJpHnfghr3T8gbx94/Pa/ecBWPSmTF0EBsPl51ri/G4mZCdyOTcZCbnJvHclhO8vvt0i/Oe33KCJVtPsu34GUqrAzf/fXtfMc995lIVlehGSp7CjJInERERCUuDhsE9r8GaX0NDjVOifMhcyJkOnmYV6sbfDFd2UkSiN0TGwqy7YfUjLdujkyBnBuTMBJcblv/wgh8qNjKCJ++dw6r9JVTWNzEmM4ER6XFENEvEVuwPLIv+9r7Atua2HT/D/tNVjMpM6LCfBE/JU5hR8iQiIiJhK3MC3PKrvo4ieFd/39kIuGSfMxKVM7Nltb5Db3XbQ0VFuFk4PMbZX8rl7rbrFlXVK3nqRkqewoySJxEREZFeYgyMvb5nrl1bBic2w4lNcHyT83PFMTBuuOJLMP+rTvELv8iIwOmA0vuUPIWZ1BgVjBAREREJKw3VcHKLP0l6x0mYSg+23dd64c3/gfyFMPT8Oql5+ak8ubb9fZ8iI1wYoL7J183BS3NKnsJMSnT/qyojIiIiIn7eBji20UmQTrzjJEzFe8B2Mak5vKJF8nT9pMHsPFnBU+uOUNvgZezgRCbnJDEpJ4mJOUmMyoznQ39Yy5qDpd38hKQ5JU9hJCkqiUh3ZF+HISIiIjKg+ayPgooCthdvZ1vRNppsE3MGz+HaYdd2fvLRtfD7Ky88CG9Ti1/dLsNXrh3LV64di7VWFfT6iJKnMJIWrfVOIiIiIt2tuLaYbUXb2Fbs3HYU76CysbJFn3/u/ScV9RW8Z8x7+ijK85Q49R0lT2FExSJERERELkxtUy07S3ayvXg7W4u2sr14OyeqTwR17nMHnuvG5MlA2ijIng4Vx6Hg7W66rvQkJU9hRMUiRERERELz0w0/5eCZg+wv34/XekO6RnFtq32VUvKDPzk5z0mUcqY794OnQHSic+yNHyp5ChNKnsKIRp5EREREQvNSwUvdf9GkXJj0btj2j5bt8Vn+JGmakyhlT4O4zr8ErzSGWGvp8i5PjXVQuIPF1Uu4OWI7OaaE4zaVh5r6forhQKPkKYwoeRIRERHpHZGuSBp8DZ13fNejMP4WOHPMP7o0DRKzW3Spbqxm3+nNREdEMyJpBB63h+LaYnaW7GRHyQ52Fr3JziHZnI6IwGUt95RX8GlrcXkboWi3swfUyc3OfeF2aKqDUddAXJpTAv30LrBe7oEWn+7nuHZTaK8CoMnXRMGZAnaV7mJX6S5KakvIT8rnoxM/qoJkXaDkKYwoeRIRERHpnMfl6VJ/l3ExKnkUk9InMSnNuUW6I7nxPzd2eJ7X5+XgmYPsirSY9FxmZc0gKSqJPac3s714OztKdrCjZAcFZwqw2HPnxUTEUNtU2/JiEc7Hcp8xPDooiUu2/JEZKx8Gb33bD77vlXbjagD2RXrYHVnB2zu/y6/2lLO3bC913rqAvrtKd/Hwwoc7fJ5ynpKnMKI1TyIiIiKdmzN4Dsf2HWv3+OC4weeSpEnpkxiXMo5YT2yLPkcrjwac1+BtYMnBJWwv3s7Okp3sKt0VmAQFIZhzNph6ZjRLnHxAuctFudtFVpOXWOskYzXGsCfSw67ISHZFRbIrMpIDkR6azlbkK3urw8dZdmQZLxe8TKO3kZLaEkrqSs7de31epmRM4ROTP4HH3bWEdKBS8hRGNPIkIiIi0rnPT/88p2pOsf7keiLdkUxMm9giWQr1M1VRbRFfffur3Rxt216Ji2VzVBQlbjfFbhelbjfeViXKhzU0ctgTgb3A0uX/783/1+6xtafWUlxbzAOXPHBBjzFQKHkKI0qeRERERDqXHJ3Mbxf/FusfnQnHfZH2RUayr5OlSAWRvTMatLRgKd+d992w/O/Y3Vx9HYAEx23cJEcl93UYIiIiImHDGNNvP/C7jZsxg8Zw26jbSOmlz3i2KbbzTm2obKxssWbrYqaRpzCRGp2KyyjXFREREekNg+MGkxSVxJn6M10+NzsumwlpExifOp4JqRMYkjCElw69RGldKUMThzI+dTyjB40mOiIagH+mTeKB1d07LS6jqYlxDY3srJnNkdqpeOuysU3JRGU+T2TKqm59rIuJkqcwoWIRIiIiIr0nwhXBF2d8ke+t/h5NtglwquSNSxnHxLSJzi11IjkJOaw4voJT1afIjs9mfOp4UqJTAq537+R7232sBUMW8JvNv+F07emQYs2Nz2Vc6jjSt6/g8urDjK1vIM3nA+B9DVM56Jtwrm/96Wsxrlrc8XsxWHzeeGyT/+b/OSrj1ZDiuBgoeQoTWu8kIiIi0rtuG3Ubs7Nmc6zqGKnRqeQn5eN2BW5he0XuFRf0OGkxafzl+r/wwsEXOF1zmtToVFJjUkmLSTt/H53K3rK9PHfgOWqbahk9aDTjUsYxJmUMSVFJAJTtvJpBtXsCru8yMCI9ngnZiSzbdZrKk3d2GE9k+lKMaTlNz+eztPHULzpKnsKEkicRERGR3pebkEtuQm6PP052fDb3Tb6vwz6T0yczOX1yu8cHxQZWmHjwlgnkTruWmEgn87n8x69TWd+IBy+NXUwFztQ0sutUBbtOnr1V0uj1MW9EKl+7bhyREYFLTBqafLhdBrerf6496yolT2FC0/ZEREREpKtGpUZB6U44tQ1ObeP3vpVkRe0jydRw2JfBZxrvZ5vN7/Q6V/zkDU6Ut71h7+5TlXjcLu6+bPi5pGq3P8k6UFSNAa4an8n/vnsKcVHhnX6Ed/QXEY08iYiIiEiXPXEHNKuUNwbAPwg01HWaRzy/4INxv2V8dhLjBycxPjuRL6wLvMyJ8lo6KtT96FsHefStg+0ef2n7KcZkJfD5xaNDehr9hZKnfig+Mj6gLSs2qw8iEREREZHw1nGJ8aGu07z9ibGQlHO+sY3kqTusO1TaMxfuRap93Q8tyF3Q4vekqCQuzbm0b4IRERERkYHN29Di155andTo9fXQlXuPkqd+aGHeQh685EGmpk9l3uB5/P7q35/bB0BEREREpE3JQ7vnOu1kT07VvjiumZDZPY8ThjRtr59616h38a5R7+rrMEREREQkXEz/MOz4NzTWtGyPS4esyZA1CTY9DrVlLY8f2wD7lkLhNji1HRPpw5qWGdR/PnUpY7OSzlXte/i1vTz82r4WfSIjXIzJTGBsVgKRES7+uvZItz/FvqbkSURERERkIMibA/csgwPLwNvoJEtZkyCh2dr5Hf8JTJ7+fU/L34cNCbj02rKnGZJ+BzE4Rcw+v3g0s4alsK+wkpT4KMYPTmBYahwRbmdi27pDpUqeRERERESkH8sc79y62a82/4r/2/J/LB66mDvH3MmMzBlcOjKNS0deXBWhlTyJiIiIiFwsTOflIIY3NnEw0hPQ3mSbeLngZV4ueJmRySO5c8yd3Jh/Y5uVogcqFYwQEREREblYZE3utMvdZypw2Y5LnO8v388P1v6ARf9YxPfXfJ+9ZXu7K8J+TcmTiIiIiMjF4oovQXzz/UMNpI6E8bfCld+E9/2dm6uqefxkIVdW13SaRNU01fD3PX/n9udu5887/tyjofcHmrYnIiIiInKxGDwFPr0GivaCyw0Z4yAyrlUnw9T6Bn5+uphTbjf/SIjnX4PzKakr6fDSl+Vc1nNx9xMaeRIRERERuZjEDHIq8+XObCNxainL6+Wz5WdY+q6X+MmMLzMjYXib/WZnziQ/Ob8nou1XNPIkIiIiIiId8vzvKK5tqORaYJ/Hw98T43k+Po4alzMWc2etr50zLemcYbTrKMlNo3st3p6i5ElERERERDrWUHnux1GNjXyzpIwvlJbzQnwcr8XFsLBiO9RVQNFuKNxB3v53eMqzntGuo6Qa59w/198N3NZHT6B7KHkSEREREZEui7OWOyuruLOyCiiCH53fXDcLyHK37J/XWNCb4fWIfpE8GWPuBb4M5AKbgS9aa1d30P9a4HvAeOAE8AvgEWs7KQfSzyxfvpw333wzqL7Tp0/npptuatH2/PPPs2nTpqDOnz9/PgsWLGjR9tRTT7F3b3BlJW+88UZmzJjRou3RRx/l5MmTQZ3/3ve+lzFjxrRoe+ihh6iqqgrq/HvvvZfs7OwWbQ888EBQ5wJ88YtfJCEh4dzvlZWV/PSnPw36/O985zstfj9x4gS/+93vgjo3Pj6e//qv/2rRtmfPHv72t78Fdf7gwYO57777WrRt3LiRF154IajzR48ezfve974WbXrt6bUXDL329NprTq89vfaCpdfeAHjtJeXCmaMAPMoHOGkygzr/vfYZxnCwRdtD3EeV8e8DVQ9vtvNaaiv2/qjPC0YYY+4Cfgs8AdwOlAOvGGPaXI1mjJkHPA9sB24Bfgf8FPh8L4QrIiIiIjKwzbq7ryPot/p05MkYY4AHgEettQ/425YCe4AvAPe3cdoXgB3Ax/wjTa8ZY8YBnwZ+1iuBi4iIiIgMVJd9AVJGwPGNsNUNVWE1uatHmb6c6WaMGQXsBa631r7UrP2XwDXW2oCSHMaYIUC8tXZXs7b/819jSOv+zc2cOdNu2LCh2+IXEREREbkovfYArGg9HdNAynDIGE9p/Aj+dSyJ45H5FHly8Bk3I9Lj+dI1Y9q8XH9jjNlorZ3Zur2v1zydTY72t2o/CIwwxrittd7mB6y1R8/+bIxJBm4GPgx8vwfjFBERERGRsxZ/B4ZeAqd3QWyqs9lu+liIjAUgBbi3byPsEX2dPCX67ytbtVfirMeKAyraOtEYMxQo8P+6AfhNO/3uA+4DyMvLu7BoRURERETEMeoq53YR6euCEcZ/397cwfZ22wInqboSeD8wCFhtjIlt3cla+6i1dqa1dmZ6evoFBSsiIiIiIhevvh55OuO/TwAKm7UnAF5rbbs1Na21ZcAbAMaY7cBW4A7gzz0TqoiIiIiIXMz6euRpn/8+v1V7Pk4hiQDGmFuNMbNaNW8HGoHsNk4RERERERG5YP0heToK3Hq2wRjjAW4AlrVzzleBn7RqWwh4gG3dH6KIiIiIiEgfT9uz1lpjzI+AR4wxZcBK4DNAGv49m4wxI4B0a+0a/2k/AJ7zlyd/Gqdi34PAcuDF3n0GIiIiIiJysejrkSestb8G/h/wIeCfQDLOHk8H/V2+Baxu1v954BZgOvC8//hfgBtsX25aJSIiIiIiA1qfbpLb27RJroiIiIiIdKa9TXL7fORJREREREQkHCh5EhERERERCYKSJxERERERkSAoeRIREREREQmCkicREREREZEgKHkSEREREREJgpInERERERGRICh5EhERERERCYKSJxERERERkSAYa21fx9BrjDFFwOG+jqOZNKC4r4OQsKPXjYRCrxsJhV43Egq9biQU/e11M9Ram9668aJKnvobY8wGa+3Mvo5DwoteNxIKvW4kFHrdSCj0upFQhMvrRtP2REREREREgqDkSUREREREJAhKnvrWo30dgIQlvW4kFHrdSCj0upFQ6HUjoQiL143WPImIiIiIiARBI08iIiIiIiJBUPLUQ4wx9xpj9hljao0xq40x8zrpP9EYs8wYU2WMOWKM+YoxxvRWvNI/hPC6ucQY84YxptwYc8IY82djTGZvxSv9R1dfO63O/Y4xRtMQLkIhvOek+99nSv3vO88ZY0b0VrzSP4T4t2qFMabSGHPQ/57j6a14pX8xxtxsjKkMol+//Gys5KkHGGPuAn4LPAHcDpQDrxhjhrfTPwN4DbDAe3DmfP4A+K/eiFf6hxBeN+OAZUAl8D7gS8Cl/nP0R+ki0tXXTqtzJwJf79EApV8K4T3HAywFZgP3Ah8BRgAvGmMieyFk6QdCeN2MAF4Fqvz9fwZ8Bfhhb8Qr/Ysx5hKc106HSVB//mysNU/dzJ8RHwJestZ+0t/mAfYAL1hr72/jnAeATwN51toaf9v3gE8BWdbaxt6KX/pGiK+bXwHXAWPOvkaMMbOAdcAN1toXeyt+6TuhvHaanesGVgPZQI61ts+/0ZPeEeJ7zt3AL4Gx1toj/rapwIvATdbajb0UvvSREF83XwG+C6RZa6v9bf8NfAZIsvogelEwxkQBnwO+B1QDkdba+A7699vPxhp56n4jgaHAc2cb/P/AS4Br2zlnMbDs7IvD7xkgBZjVM2FKPxPK62YH8FCrN5A9/vtORxxkwAjltXPWF4AEnA/EcnEJ5XXzLuDls4mT/5zN1tpsJU4XjVBeN1FAI1DbrK0EiPcfk4vDdcDXgP9HcH9z+u1nYyVP3W+0/35/q/aDwAj/N71tndNW/+bXk4Gty68ba+2vrbW/atV8k/9+dzfHJ/1XKO85GGNGAg/gTL+q77nwpJ8K5XUzGdjtX69yyhhTb4xZYozJ69FIpT8J5XXzV8AL/NAYk+KfIfF54D/W2roei1T6m/XAcGvtL3Cm4nWm3342VvLU/RL9960XwlXi/PeOa+ectvo3v54MbKG8blowxgwB/hfYALzerdFJf9bl145/6s3vgT9ba1f0bHjST4XynpMOfBRnhOFu4EPAeGCJMSaih+KU/qXLrxtr7QGcNblfwhlxWgecxnktyUXCWnvcWlvehVP67Wdjvdl1v7NrBtrLqn3tnNOV/jLwhPK6OX+ykzgtw/nj9V7NIb+ohPLa+TjO9JubeyQiCQehvG48QCRw3dkPQcaYgzjfKN8GPN3NMUr/0+XXjTHmHuB3OAv+/46zxvJBnKR7sbVWI9/Sln772VgjT93vjP8+oVV7AuC11la1c05b/ZtfTwa2UF43wLlqaatwvom5yv8tn1w8uvTa8SfaP8ZZuFvjHzFw+Y9FGGP0d+HiEMp7ThWwtvm3x9baDTjV1ib1QIzS/4Tyuvkq8KK19uPW2tettU8A1wOXAR/ouVAlzPXbz8b6I9n99vnv81u15wN7Ozinrf5wvgCADGyhvG4wxswB3saZT365tXZrz4Qn/VhXXzuLcP4A/RNnEXcj8JD/WCPw7R6IUfqfUN5z9uOMPLUWQXBrGCT8hfK6GQKsad5grd2NM4VvfLdGJwNJv/1srOSp++0DjgK3nm3wl/G8AWdaVVuWAYuNMc3nCt+K88ayuSeClH6ny68b/54aLwGngEustfva6icDXldfO8/jVCpqfvup/9gsnKk1MvCF8rfqVeBSY0x2s3Pm41RNW9VjkUp/EsrrZi9wSfMGf8GaVJyy5yJt6befjbXmqZtZa60x5kfAI8aYMmAlzl4GaTgbw53dMC7dWnv2m5hfA5/F2WjwJ8AUnHKOX7XWNvT2c5DeF+Lr5mGcqXqfBvJaVbw6bK092VvxS9/p6mvHWluC88fnHGPMZf5rbejV4KXPhPie8zPgY8BLxpjvALHAT3ASp1d7+SlIHwjxdfMg8LQx5vfAU0AWzr5PBcCfe/UJSL8VVp+NrbW69cANZwfkI0ANzh+Wec2O/cn5T9+i/0ycN6E64DDwlb5+Drr139cNzsLtRpypMm3dvtTXz0W3/vnaaefcz3d0XLeBewvhb9UInL1WKoFSf5/kvn4euvX7181twCacbRGOAH8AMvr6eejWZ6+f7wJVrdrC5rOx8QcnIiIiIiIiHdCaJxERERERkSAoeRIREREREQmCkicREREREZEgKHkSEREREREJgpInERERERGRICh5EhERERERCYKSJxER6ZeMMR8xxth2bnXGmOPGmP8YY+b2dazN+ePb3Oz37/rbbu27qEREpDtE9HUAIiIinXgTWN6qLRmYA9wK3GiMWWitXdG7YYmIyMVGyZOIiPR3y621323rgDHmQeBbwI+BS3ozKBERufho2p6IiISz7wONwDxjTGxfByMiIgObkicREQlb1toG4Iz/18iz7caYK40xS40xZ4wx1caY1caYO9q6hjHmCmPMC8aYYn//VcaYW9ro92FjzHJjTJkxpsEYc9IY81djTH7PPDsREelvlDyJiEjYMsbMANKAI9bacn/bPcBrwGTg78D/ARnAP4wxX291/geB14ErgJeAPwJDgGeMMR9t1u9/gcdx1lr9CXgEOAG8H1hujInpqecoIiL9h9Y8iYhIWDHGGCAJZ43TL/zND/iP5eIkNruBy621Jf72b+AkVN8zxjxnrd1ujBnk71vi77vX3/cHwBbgJ8aYJ3ASry8AbwFXWmu9zWJZAlwPXA682qNPXERE+pxGnkREpL/7TvMy5YAPKAOW4Iw6/Ze19o/+vh8EooBvn02cAKy1tcB3cP7u3eVvvh4nCfv52cTJ37cYJ1n6MRAP1AEfAj7XPHHye9N/n9FdT1ZERPovjTyJiEh/17xUeSLwbiAXeAK4z58YnTXDf7/IGDOx1XXi/fdT/fdT/PerWz+gtfbpVk1PGmNc/muOA/L95y/2H3cH+2RERCR8KXkSEZH+rkWpcmPMt4AXcUaZzgCfadY32X//iQ6ul+K/H+S/r+gsAGPMbcCPgFH+pipgI870vsWA6ewaIiIS/jRtT0REwoq1thp4D1AIfNoY8/Fmh6v89yOstaad24xWfRNaP4YxJsoYE+H/eQ7wD5zpgO8DRgKJ1toFOOuoRETkIqHkSUREwo61thD4pP/Xnxpjhvl/3uq/n9n6HGPMKGPM/xpjbvI3bfPfz27jIb4E1Bpj5gPvxfl7+Slr7d+stQestdbfb9zZy4f+bEREJFwoeRIRkbBkrf0P8G8gFviNv/kJwAv8wBiTdbavfxTpl8B/Aan+5meAauBzxpihzfqmAB8HKoE1OAUjADKbP74xZhFOqXIAT3c9LxER6b+UPImISDi7H2fN0rXGmPdZa/cBX8aZWrfDGPNHY8xDOCNS1wAv4CRYWGtLgU8Dg4F3/H1/AWzH2evpbmttPc5eUY3Ar40xjxtjfmSMeRGnNHmZP46zCZmIiAxgSp5ERCRsWWuPA9/w//qwMWaQtfanwA3AZuB2nFGkRpxRpzustU3Nzn8cuBp4B7gDuBc4BNzgH9nCWrsZp6z5RuBW4D4gC/g2TsU9n/+4iIgMcOb8tG0RERERERFpj0aeREREREREgqDkSUREREREJAhKnkRERERERIKg5ElERERERCQISp5ERERERESCoORJREREREQkCEqeREREREREgqDkSUREREREJAhKnkRERERERIKg5ElERERERCQI/x8CXqrp6wambQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "c = 0\n",
    "test_set_performances = []\n",
    "labels = ['Pancancer', '{} Averaged'.format(cancer_type), '{} Patient'.format(cancer_type)]\n",
    "data = postprocessing.get_training_data(avg_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "\n",
    "# remove overlaps and construct y_true\n",
    "test_genes_pos = avg_test_genes_nooverlap\n",
    "test_genes_neg = avg_test_neg_nooverlap\n",
    "test_genes = pd.concat((test_genes_pos, test_genes_neg))\n",
    "y_true = np.concatenate((np.ones(len(test_genes_pos)), np.zeros(len(test_genes_neg))), axis=0)\n",
    "\n",
    "for model_dir in [pancan_model_path, avg_model_path, patient_model_path]:\n",
    "\n",
    "    pred_all, _ = postprocessing.compute_predictions_competitors(model_dir,\n",
    "                                                                         network_name='CPDB',\n",
    "                                                                         plot_correlations=False\n",
    "                                                                        )\n",
    "    pred_test = pred_all[pred_all.index.isin(test_genes.Name)]\n",
    "    pred_test = pred_test.reindex(test_genes.Name)\n",
    "\n",
    "    pr, rec, thresholds = precision_recall_curve(y_true=y_true, probas_pred=pred_test['EMOGI'])\n",
    "    aupr = average_precision_score(y_true=y_true, y_score=pred_test['EMOGI'])\n",
    "    plt.plot(rec[1:], pr[1:], lw=5, label='{0}'.format(labels[c] + ' (AUC={0:.2f})'.format(aupr)))\n",
    "    c += 1\n",
    "\n",
    "random_y = y_true.sum() / (y_true.sum() + y_true.shape[0] - y_true.sum())\n",
    "plt.plot([0, 1], [random_y, random_y], color='gray', lw=3, linestyle='--', label='Random')\n",
    "plt.legend(fontsize=20)\n",
    "plt.xlabel('Recall', fontsize=20)\n",
    "plt.ylabel('Precision', fontsize=20)\n",
    "plt.tick_params(axis='both', labelsize=16)\n",
    "fig.savefig('../data/cancer_specific/{}_pr_curve.svg'.format(cancer_type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3076923076923077"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Set Performance for all Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n",
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:701: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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M4BLbfnePr4NUZ9CUiFwOXA6QOPhyYrv6egu1b4PHlIAWjeNYusl/8bgWQSgtNWzdXbtzOQuLS5k8fzMXHNKB6MhwOmYk7pNr6MY1LhuRm7drG5HN2vhN6xr5iggxSY38pqsr4ZFRdDnyTLoceSZ5WTsoLsglJrlJucFDezPtlJ24xoHPDQ61jVllNyPNkmMqrdVlJNvadGmpYVuQI2GH92zKo2f0dLfMzF2XxZVj/yIrN/CgfHj3dHc+vp+3Nah81CYNovuu+6o5Otflc+BSbG30Jee104DvjDHZIj47mHZhg2EsUNUQ0Hjn0bXSuiuo+rrShQH+FhYdXcV53IwxY4AxAE0v/TRk80mXbiobANE2Lb7SINomzRbL+h05Aa+okhwXSdu0BNKTY/ihijvuXR538a7BIvuapIyyoJmzYwtJlQTRnO12bFpco/RKFz0IhdiUJkCTcq+Z0hKy1ttF9FNatPexV8OwwmOqVasmcZUG0dbOoLaNu/ICanb1dvbAVtx9YjfCwuw1ZuriTG744O9qrzB0SFd7szVv/e56HVDksm/+t6ma+BFbSz0ZQEQ6AT2BzyrZZ6Xz2LmSNC7dnUfXOPX1QAnQWkTKdV4ZY4qNMeL5A/wd0LtoAP7yWOFlQCf/8xoTYiLo0SoFsGvdBurFS/oz6e4jeOfawTRJqLzfz7XGLsCmXfvmqkWN2pR9vLav8p4hVaYoP5fdm1YB0KT9fnWeL0+Zy/5mwddj+fPDZyipZFTw9pULKcyx95tpnXuHKHfVN299lvt5v7b+a/Tx0eF0bWabzf9cU/1Vgc4a0Ip7T+ruDqCfzFrP1e/MqXYAbZcaR5ozeO6P1aFfncgXDaL/MsaYIuw0lmEi0piyptwKU1s8jHceTwrgFK5FSMc758vFLswQD1RveZgGbv2OXOastoH05P6t/NYAzxzUlgjnq5y++yvwb5dwBdywMOHsIe38pktLiuaIns0Au47u5n00iMY3bkpKq44AbPhrut8gte73HzGl9uLbvJevrv+6k525geU/fc6633+sdGH5ZT99Dth+3uY9Q5vH6ti4K5/5zqLwx/Vu5nfN2pP7tnB/hn9YWPVSlJ4O6tiYu0d0c//+8k8rueeLRZUOYvKnp8f3nS7YWPVUmFDQIPrv9Dm2Kf947AILE40xlTXTvgPsBq4TEb9tbCJyCjAAmGOM8Rwi+rLz+D9n5aLK7FOfyTen2JUUmzeOY/QZ+1fY3jEjkVtOtJXzVVv3MqkaAyE+nrmGHGfw0qhju9GtRVKFNPHREbx25UHEx9iemee/C+03WNS29kOOB+zqRQu+fKPC9r1b17PYGfEan9qMjO79Q5q/Zj0GuBdrWPTtuz7nfy794RMyl9jVozocciKRsfEV0jQk7/9qv4w8IzmG24/rWmF7u7R4rj3czopbsz2HqUsC/07chOgI/nd6T8KdGujYGWt4dlKlq49WqktG2SCyBRsCGaJR97RP9N9pInZloquBvsDTlSU2xmwRkRuxKxJNFpGTvFckcgLoO9ipLBd67f+ViLwFXARMEpHzjTFrvfZvBjyAbVpucOvk+vPJzLWcM6QdB3VO45LDOtImLZ63p65kZ3YhB3Zowg3HdSMlPoqSUsNt7/1VYUL7sxcdyFmD2wJw/Zu/8/HMsmLZtqeA0Z/+zePn9yUpLpLv7jyC1yYv45el28jOK6J3u8ZccURnWjv9rV/MWscnM8sV6z6n9YGHsXbWD+xYtZBVP08gZ8cW2g06lqj4RHauWczSHz6xo14ljN6nXV1hUYY/P3iadX/8BECfs0fRpn/tDjKLTUml3eBjWTXjG7LWr2DaM/+h47ARxDfJIG/3DtbO+sH99WyNWnemyxFn1Or568L4vzZx6oEtOLBdY84d1JqWjWP5aNZ6snKKOKBNClcc1p7k2EhKSg33jV9U4TP8yOk9OLmvXXbxjk/n88WfZVPJzxvcmqbOgKQNO3P5Zu5md7NwZVZmZlNUUvEy0Dat7IYkc0/994eCBtF92TB/i8R7+AjbVFuOMSZfRL4FzsAGvcqacl37vCUi4djBSHNF5AfswgrRwFBsMN4CnG2M8dXOdQV2gNKNwEoRmQIswPaX9gQOBSKBJZQtzrBPuOjFmXx4w1AOaNeYI3o2czetuhQWl3Lru38G9I0s3t6ZtoroiDDuOX1/YqPCuf7Yblx/bLcK6d6asoK7Ppob7FtoMESEARf/l5mv3kvW+hVsXfxnhe8MlfAIep9+NeldetdLHnueeDG5u7axZcEssjasYPZ7T1ZIk9apF/0vvJ3wyNDNYa2J696dy2sX9aVnq2QO6ZrmHrzjUlhcyujxi/h1hf9vevHljP4t3c9bNo7js+sOCmi/wx+dxsZdFYNkRpLtD80vKvEZZOuDBtF9V1VTXADmOj++fI4NopOMMQG1ixhjXheRn4DrsFNhhgKFwArsAvJvGmN8/pc5fbE3i8i72EXoD8UuPB8NbMbOUf0U+MoYE9ovBKyhXTmFHPfIT5x3cDtO6d+aLi2SiY+OYOvuPH5ekskrE5expJKRu1V57ccV/LhgC5ce3omDu6W7p8tsycrj12XbGDt1pc8v7N5XRccnccioJ1jz20Q2/DmNPVvXUVyQT0xSI9I69aLTsJMrHblb18IiIhl48Z1smDOdtbMmk7VhBcX5eUTGJdCoVSfa9D+c5vsPxs9I9wYpK7eIs16exekHtuT43s3o2DSBuKhwtu0tYNbKnbw1Yw3Lq/mlCSlxkTRPqd35sa5uiz15DWcZRanJEmRKNTShnOKi4KJTKvYDq7o1fnr9LnP3b7Xkf8N93hXtU4M4lFJKqYZEg6hSSikVJA2iSimlVJA0iCqllFJB0iCqlFJKBUmDqFJKKRUkDaJKKaVUkDSIKqWUUkHSIKqUUkoFSYOoUkopFSQNokoppVSQNIgqpZRSQdIgqpRSSgVJg6hSSikVJA2iSimlVJA0iCqllFJB0iCqlFJKBUmDqFJKKRUkMcbUdx6UqjVb9hTpBzqE1m3Pre8s/OsUlZTWdxb+lQZ3aiS+XteaqFJKKRUkDaJKKaVUkDSIKqWUUkHSIKqUUkoFSYOoUkopFSQNokoppVSQNIgqpZRSQdIgqpRSSgVJg6hSSikVJA2iSimlVJA0iCqllFJB0iCqlFJKBUmDqFJKKRWkiPrOgFL1rbi4iK/HfcbkiRNYvXIFRcVFpKWl03fAQZx21nm0adu+3o9fUlLC99+M56cfvmfViuXs2Z1FUlIyXffryYhTz2Tg4KFVHmPmjKlM+HIcixfOZ3fWLmJi42jXoSPDDjuSE04+neiYmBq9z9pWXFzMlO++4NepE9mwZiXFxcU0apJGjwP6M/yks2jeqm3tnq+oiLuuO5+Na1dx71Nv0LFbz4D3/eWn73nl8Xvo2Xcgtz74XK3mK1SKi4uZ9v14Zk2byMa1q9zl3b33gRx54pk0q4Pyvm/UhWxct4o7n3idDl17VLnP2hVLmPLdFyxbOJdd2zMpLS0lKaURHbv14pDhI+jaq2+t5jEQ+lVo6h+lul+Ftjsri1tHXcmSRQt8bo+Kjubm2+/h6ONHBJWf2jh+5tYt3HHTtaxYtsRvmuNGnMIt/x1NWFjFxqXi4mIeHv1ffpz4rd/927brwP+efpFmLVpW8m4qqquvQtu7J4sn7r6BVcsW+dweGRXNRdfextAjj6+1c37w2jN8N+4DgGoF0cwtG7l31Eiy9+wOSRCti69Cy96zm6fvvZHVy/2X9wXX3Mrgw4+rtXN+9PqzTBr/IUCVQdQYw6dvvcDELz6gspg1+IjjuPCa24mIjKy1fLqP7eer0LQmqv61SktLufvWG9wBbtgRwznm+JNISEhg3t9zeP+t18jO3stjD95LekYz+vTrH/Lj7927h1FXjGTTxg0ADBg0lONPOpUmqWmsXL6Ud98cQ+bWLUz4chzpTTMYednVFY7x8rNPuANoWnpTzh15Ge07dmLH9m1M+PJzZs/6lTWrV3LrqKt4/b1P671GWlpayrMP3OYOoP2HHs7BR55AbHwCyxbO5euPx5Kbk80bzz5Ek/QMuu/fr8bn/Orjse4AWh07t2fyvzuuIXvP7hrnob6UlpbywsO3uwNovyGHM+SI44iLT2D5wr/55tO3ycvJZuxzD9M4LYNutVDbm/DJ2+4AGoivPnyD78e9D0BK4zSOOuks2nfZj7CwcNauWMKkLz9i25aN/DJ5AmFhYVx0/Z01zmOgtCaq/lGqUxP99qsvePSBuwE467yRXDXqlnLb165ZxbWXns+e3btp16ETb37wuc+aXl0e/8lH7uOrcZ8CcPYFF3PldTeV275zx3YuOfc0du7YTlR0NOO+/YnEpGT39swtmznrpKMpKSkhvWkGb7z/OUnJyeWO8diD9zDhy3EAXH3Dfzjz3AsDfo91UROdPulrXnv6AQCOPfU8zr70+nLbN61fwwM3X0r23j20bNuBh158v1p/F0/FRUW8+8qT/PTtuHKvB1ITXb54Hs8/9F927ch0v7Yv1kRn/PANbz37IABHn3IuZ1x8Xbntm9ev4eFbLydn7x5atOnAfc+/W6Py/mDMU0z97otyr1dWE92euZn/Xn4GxcVFNG3Rmjsee5Wk5Ebl0uTl5vD4ndeyZvliAP77+Bg6dusVVB790S/lVsrLJx+8A0Djxk24+IprK2xv07a9u2a3euVyZs2cEdLjZ27dwjfjPwdg/wP6VQigAI2bpHLeRZcBUFhQwMyfp5Xb/sesmZSUlABw7shLKwRQsIHTdVGcOWNqNd5h3fjuC1sjTG7UmFPPv7zC9uat2nLyufY9b1izknmzZwZ1npVLF3L/zZe6A2hYWHhA+xUW5PPF+6/z0K1XsmtHZsD7NVSuGmFSSmNOcsrVU7NWbRlxzqUAbFy7kvl//hrUeVYtXcjDt17uDqCBlttvUydSXFwEwNmX3VAhgALExsVz4TW3uX+f+eN3QeUxGBpE1b/ShnVrWb1yOQCHHH6U3ybMY44/ifBw+88+dfKkkB5/yg/fU1pqax2XXzPK77kOOexIjjr2BE4/5wLS0jPKbdu1c6f7eavWbX3un5CQSHpTu9/O7dsreVd1b8vGdWxYsxKAA4ccTlS073IbeuTx7ovwrBk/Vvs8H7/5AvfdeDGrnZpLn4MOZvhJZ1W539ZN67n1stMZ994YSoqLiY2L58Z7H6/2+RuKrRvXsXGtLe9+gw/zW96DDz/OXd6zf65+eX869kUeuuVSd03xgIEHc+SIMwPad9nCuYAdP7DfAf67VNp07Ep8YhIA61cvr3Yeg9XggqiIjBQR4+cnX0Q2isgXIjKwAeT1BidfI+s7L55EpG0lZej580x95zUQIpIsIhWrcjUw/+857ue9+x7oN11cfDwdO3cB4K/Zs0J6/FkzfwYgvWkGPfY/wO8xUtPSufO+R7j2xlsr9KumpqW7n69bs9rn/gX5+WzfZpskm6Sl+T1PKCxbNM/9vFvPPn7TxcbF06ZDJwAW/z272udZsWQBxhgSEpO45IY7ufGeJ4iJja1yv53bM9mxbSsAvfsP5n+vfETv/kOqff6GYvnisvLuWkV5t2rvlPe8P6t9nlVOeccnJjHy+v9y3V2PER1TdXkD9B96BMNPPoeDjxpBeHgVw3iczpyiosJq5zFYDXlg0TRgqtdrKcAA4CTgeBE51Bjzc2iztU9ZC4ytZPtvIcpHTS0DNgMv1NYB165Z5X7eslXrStM2b9GKpYsXkbl1C3l5ucTGxoXk+KtWLgOgS7f9yqXPzclh+7ZM4uLjywVJXw4cOIioqCgKCwv58N03OfLY40lISCyX5s1XX6C4uBiAYYcfVeV7q0ub1pUF+qYtWlWaNr1ZS1YvX8KObVvJz88jJsCLMkB8QiLHn34Bx59+gbv2Ehihc/f9OencS+nZZ0A19muYNq9f436e3ryq8m7B2hVL2LltKwX5eQEHQYC4hCSOOe18jj3tfOITqlPeMOSIwEZgr1+9nJzsPQA08WqRqUsNOYhONcaM9rVBRO4H7gYeAwaFMlP7mDX+ynAfk44NorVme+ZW9/OmGc0qP3nTsn/I7ZmZtGrTts6Pvzsry90Um9GsOQDTf/qBj99/mwXz5pbb96TTzuL0cy4gKiqqwrGbpKZxxbU38vxTj7J1y2YuPvsUzh15Ke06dCJr104mffs1M6ba5rkBg4Zy3IhTq3xvdWnXjm3u56lplV8IG6c2LdtveybNWrYJ+DzX3/VoUINjuvTozd1Pvlbt/Roqz/Jukt60kpRe5b1jGxktKr859HTNfx8JejBSoCZ8+o77eY8Q3uA05CBamQeB24GDRCTOGFM3k9XUP9aePXvcz2Pj4itN69nMl529NyTH3521y/1aQmJiuVG6njK3bmHMi88wc8ZU/vf0i+VG5rqcdvb5NGvRitdefIbVq1bw1P8eKH/+mFguueo6Tj79bCLrYH5ddWTvLSu3mLjKa/yeNaHcnOxqnSfYC3pdB4JQy/Es7ypaWMqVd4D/By51XW5//Pwjv0//AYCklEYcdOgxdXo+T/vkJ8IYUwi4Jma5b79FJFJERonIbyKyW0QKRWStiLwiIuU6e5w+wbEiMkhEpopItojsEpGPRaSt9zlFZISI/CoiOSKyXkTuBHwOLxORziLynohsdfKwUkQeE5Fkr3RjRaRYRJqIyGsisk1E9orIRBHpICLRIvKoiGwSkT0iMkVE9q9p+fkjImeKyC/Oe8wWkZkicpZXGld/6/0i8pyTdruInO5sFxG5UkT+EpE8p0y/EpEKnXoicpSI/CgimU7a+SJyh4hEOduHiYhrysr+znlH18Z7LSq0fSbh4eFERFR+LxntMdiisDCwvpaaHj8vr+y+8NuvvuCrcZ/SvEVL7n34Cb75aSYTZ/zB0y+9Qfee9uOwYN5cHrzndp/Hz87ey7y5f7J9e6bP7fn5eUz7cRJLFs0P6L3VpWKnLyssLLzK/q+o6OgK+6nqKS6yo14DKe/IKM/yLqrTfFXHisXzeOPpshvDMy8ZVeUNQW3aJ4OoiPQFUoF1xpgsj00fAs8ARcAY4FUgH7gC8LVcS19gClACvATMA84AfhQR9ydGRC4FxgPtgXexfbV3AuUn/tm0A4C/gLOBX7H9eJnAf4DfRKSx9y5OHgZh+y9nAkcB3wCfAWcCnwLfA8OACSJS658QEXkC+Aj7Hj/AlmU74EMRedTHLpdjy+plbN+qq3/1bee1KOAVJ+8HAzNF5DCP8w0Fvga6Ah8DzwPFwMPO/gBrgPuc51ud51Nr+l4BwsJdH32fU7/8EgksfU2Pn5+f735t65bNtGjZilfGfsRhRx5NYmISMTGx9DlwAM++8hY9e9sBIb/9MoPffple7ni7du5g1BUX8dG7b5GTnc1Z543k3U+/ZvLMOYz7bgo33X43KY0as2DeXG66+lJ++6V603hqm2sEaIDF7KHaOyjKPqfVLu8GUtzLF/3N06NvorDA/r8MO+ZkDjr06JDmYZ9pzhV7dUnGBhvXbOb7PLYPBE4F3jfGnOfxegQ2qPUTkc7GmGUeh+0B3GqMedzjHN9jg9ihwPcikgI8AWwADjLGbHDSPguUu2KJSDg2yEYDxxljvvfY9j/gNuBx4BKP3cKAXOAQY0yBk/YX531GAz2NMXud198CRgKHAIFMhGpbSc1tqjFmqnPcocDNwBxguDFmm/N6GvATcKuITDDGeL7fdKC3McY9vM+pjZ6PDcIXGmOKndcfAWYD74hIe6clYRQ20A4xxqx20kUCvwMXisiNxpg1wGgRuRfYUpv9u67BOyUlxZSUlLinmfhSUFAW0Hz1O9bF8aM9alkA19x4G8kpKRX2jYqKYtQt/+XS804DYNJ33zBw8MHu7U8+cr97ucA77n2Io449wb2tSWoaI049k/4DB3P1xeeyc+cO7r/zP7w/bgKNGjcJ6H3WNlfTdklJCaUlJYRVUm6FBQXu55EB/l1Uea4m2kDKu6jQs7yj/aYLlXl/zOSl//3XHUD7DjqU866sUK+pcw25Jnqv53QMoBTYBUzA1kJvNsa86ZF+AzbA3ON5EOdC7hrB6z2UMQ941iOtoSw4tXUej8UG72ddAdRJOxtb6/I0COgEfOgZQF3vB9gInOtZy3W87AqgDtfs8TGuAOpwzYFoS2DaOOf19TPMI91I5/EWVwAFcJ672ggv9jr2Cs8A6nDdHNzgCqDOcVZja5ctgCOdl12fvf4e6YqAY4Amxpiyzpo6EBdf1k+Zn5dXaVrP7Uk++hzr4vhxHv2o0dExDBg02O/+nbp0Jc0ZFLJ4QdmfZPu2TH6e9hMAffsPLBdAPTVr0ZLLrrkBgJycbCZO+KrS/NYlz2a4gvzKy81ze0JiYH8XVV5MbNnnzPNmzhfP8q7eiObaN+XbcTz3wH/cAbT/0CO48rYHKr0JqCsNOYhOw9Y07wOexgZJgPeAZsaYpzwTG2M2GGPeBtaJSB8ROVtE7hKRz4FznWTeJbzWqRV5cvW1ugKdqw/S12Q076VSejuP071exwmSfzjH7eq1eYXX7znOo/fEPtenPNDbwGnGGPHzM9or36WU3Wx4cr3m3Rfra9JhXyeP14jIaM8fyt5zb+fxNeysro9EZJmIPCsiRwM7jTHVWohURC4XkdkiMvvdt14PaJ+mGc3dzzO3bqk0rWu7iNAkNbB5lDU9fuPUVPf2lEaNiIiofMCPa4Tv7qws92tLFy10L9Y9aMghle4/9NDD3c8XLay/vtHU9LKRzDu2b60kJex0tosIKfVUc97XeU4F2bmtGuXdqH7K2xjDJ28+z7svPUZpqV2J65CjT+Ly/9xf9RzSOtKQm3PLTXERkbux/ZrnYQNdhcn3InIFtibquoJlYfvqFmPnl3q35BdQkWsgiyuta40pX8PRdnr97ro98xcENjmP3n2aOd4JK8lfXUgC8n3cUGCM2S0iuVTMs69qQgr2M3VvJedq7Bz3OxE5FNtXfARwvfOzU0RGG2OeDzTzxpgx2D7wgNfObde+g/v5pg3radeho9+0mzauByCjWYuAF2ev6fETEhJpmtGMrVs2s3dv1ZXyImegR2JSWQ0h12NwUkIVNYfExCQiIiIoLi4mp5ojL2tTizZlXwuXuXkjLdt08Js2c7O9r05t2szvSjuqci1at3M/z9yyoVz5e8vcvBGAJun1U96lJSW8/vT9/DZ1ovu1Eedc6l6SsL405JpoOcaYHOxAlq3Yms4Vntud/rhXgO3YxRhaG2MaGWOOwfb1Bcs118BXe1GC1++uq08LP8dyBeQdNchPXdgLxDn9v+WISAwQS2B5zgbWV1L7FWPMza7ExphpxpjjgSbYZtwXsf2kz4lInY5R79ajbHHqeXP9r8CSk53NimVLAeh1gP8VXeri+N2dY+Tm5LBm1Uq/xyguLmb9Wtsw4JpTCrYG67Jxw/pK85u1a6d7wYVGjbzHvoVOh65lC0ssXTDXb7q8nGzWOssqdtmvdx3n6p+rfZey8l6+8G+/6fJyc1i/ypZ353oob2MMrz11nzuAhoeHc9Gou+o9gMI+FEQBjDFbgaucX5+S8lNRznE9GmO+NMZ4XjW6OY/BjClzXQF9dUp5fwfTXOexwjpgIhLmvJ6NXUmoIZnrPPpav2wIttwWBnCceUBLEakwS15EjhORB11TdMRORXoA7A2SMeZ7Y8y1gOu7vKr+lukaaNa8hXsloB8nfed36sr3E750L+A+dNjhPtPU1fEPO6rsPmL8Zx/5Pdf0KZPJc/pVhxxSdozu+/VyT6/56Yfv3It4+zL5+wnu570OCP0XG7ukNW1Ou0723/XXqZPcU4W8zZg8wd2c13fQsFBl7x8ntWlz2jrlPWvaJL/L5f3yY1l59zmo8q6BujD+/deYNc2uLR0ZFc01dz5aq98lWxP7VBAFMMZ8AYzDNi++7LHJ1V9YbtkNEbkAO5oVIJiZ5N8C24DrRaSzx3G7At63QT9j+zdPEZFjvbbdB7QCPvEaRNQQjHUeHxGP+bTOc9fq2u8GeBwBXnDN9XSO0wzbSnAHZbX14cCdUnEN5LbOo+eNRhEe84Fryyln2PuubZlbeemZiouIr12zirGvvQRAi1atOaiKfsXaPv6goYfQtp1tzvzy84+Z/tMPFY6xedNGXnjKzkCKj0/giKPLPnbxCQnuwUQb1q3lhacfdy9o72nxwvm8/optPU9OacThR3l/dEPryBNOB2DXjkw+eP3ZCts3rV/DF+/bVYOaNm/FAQP23bVrG4LDj7cju3ft2MbHr1f8GrfN69fw5Qd2rEF6s5bs39//ILe6sHLJAiZ8YsdwighX3vpAg1qvuCH3iVbmemw/2tEicrYx5kPsgKOzgC9E5ENgD3bk5yHYeZrp2GbDajHGZIvIZdg5m7+LyGfOptOxwTXFI22piFwITAS+FpGvgZXYUbsDsX2z/6n+261bxpjpIvIUcBMwz8k3wPFAM+BRr+kt/owFTsRONZovIhOxn7EzsGV/uzHGtajsvdhpRFNE5FPsyOXuwAnYcnrP47gbga4i8jLwrTHma2rB8ONOZMJX45g350+++PRDNm3cwIhTzyQ5OZkF8+by7luvkb13D2FhYdx0290VFk14ZPSdfD/hSwBuv+dBjjnhpFo9fkREJLff+yCjrriIgoJ87r3jZo465gQOPXI4CYlJLJw3l/fffsO9utF1N99eYWrKFdfdxJzZf7B50wa++OQDli1eyAknn07rtu3Iy83lt5kz+PLzjyksKEBEuOW/9xKf4N1LEVpDjjiOaZO+YumCuUz++lMyN2/k8ONOJSEpmeWL5vHVx2+Rm70XCQtj5LW3VRhQ8uqT9/HzZFuzvuymezi4gdRYGqpBhx3LjElfs2zhXH6a8Bnbtmzk0GNPISEpmRWL5/PNx2PJzbHlff41t1Yo7zeevp9ffrTT8C++4a6A17oN1Lh3X3HXgnsPOJgm6RmsW7Ws0n3CIyLL9ffWpX0yiBpjNopdMeh54BkR+d4YM0Hs6jq3YQcf5QKrgGuwix78hZ2uEvjXqZed70sRORwYjV38IBc7kOUP7EIBnmlnisiB2AFORwBHY2tVD2KDUfXWJwsRY8zNIvIXdsDWudja31zgWmPMuMr29TiGEZHTsGV+EbamnodtCn7KGDPeI+0fInIwcBdwGJCGHXj1LPCg0wfuci12bvDF2NaEWgmiIsKDjz3Lf66/gqWLFzJr5owK3+kZERHBTXfcQ78BB9XL8bvt15PHn3+V0XfczM4d2/l+wpfuwO0SHh7B1TfcUiGIA6SkNOLpl17nrltvYMWyJSyc/zcL51fs+4qNjeU/d93HwYceUe33WdtEhFF3P8bjd41i9fLFzJs9s8J3hoZHRHDRtbfTo5KvxlKBERGuvfNRnrr3BtYsX8z8P3+t8J2h4RERXHD1bezXO7TlnbllY7lv6Znz2zTm/Datkj2sJukZPP7m+DrMWZkGF0SNMWOp/JtHXOlewOtbPYwxH+MV1DyIV1qf/aP+zu/UxA7zfh34xEfaJZT10fpljBlJ2RxNz9dHYwN2QHnzkW4NQfT/GmPeB96vybGNMSXYgFexXahi2j+AEQGkm4CdH1zrklNSeOnN9/lm/GdMnvgta1atIC83lyapaRzQbwBnnnsh7Tt2qtfj739AX9777Bu++PRDfp72ExvWr6WwoJCmGc04oF9/Tj797EpH/zZr0ZJX3/6IyRMnMOWH71m2eBF79uwmJiaWFq1a0f+gIZx8+jk08ZhWU98Sk1K49+k3mPLdeH6dOpGNa1eTn59LSuNUuu/fj2NOOZdWbf2P3FXVk5CUzJ1PvMb0iV/y29RJbFrnlHejVLru35fhJ51Dy3oo73Urlob8nNUlrnlkSv0TBDrFRdWOddv1ux9CraikYr+2qnuDOzXyWXnY5wYWKaWUUg2FBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIYY+o7D0rVmtj+t+gHWilV6/J+f0J8va41UaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIEfWdAaUauojwMC45eSBnDj+A7h0yiIqIYNO23fz4+zJe/GgGy9Zuq9Hx27dswnVnHcxhAzrRqmkj8guLWLNpJ19Onc+bX8xi267sSvef8MLlHNa/c0Dniu1/S43yGioNvcwBwsKE848/kNOO2J/9OjajSXIcO/fkMnvhel77/Fcm/bqkRnkMNS3z4OhXoal/lNr+KrTGyXF8+cyl9Nuvtc/teflFXPfo57w/YXZQxz/vuH48d9upxMZE+ty+bVc2l933ERNn+r84rJ90H6kp8QGdb18IovtCmbdIT+azJy+md5cWftO89eUsrnn4M/aFa6yWedX8fRWaBlH1j1KbQVREmPjylQzt0wGAzyf/zTtf/8Ge7DwG9W7Hf0YeTkpiLEXFJRx/3Rim/7myWsc/6qCufPH0xYSFhZGbX8iz70/j5zmrEBEO7tOB6885mJjoSHLzCzn8sheZu3RjhWO0TE9m+Td3A/DQa5P4auqCSs85b/mmauUx1PaFMk9OiGHmOzfQvmUqAN//spi3vpzFlu176NmpObdddDitMhoB8MCYiTz8+g81LJW6pWUeGA2i6l+hNoPo+ccfyJh7zgTg6Xen8t/nvym3vXObNH56/VqaJMezYMVm+p/7VMB3wCLCgs9vo33LVAoKizn00ueZs6T8xWPwAe2Z+NKVhIeHMfm3pZxw/WsVjnPskO58/tTFABx80XP8sXBdEO+04dgXyvy520/lslMOAuDJd6Zw1wsTym1Pb5zAb+/dRLPUJPLyi2h/3P1k7c0LtAhCTss8MPql3EpV0/XnHAzAlh17uH/M9xW2L1u7jYdemwRAj47NGD6oa8DHHtavo/uu+pVPf6lwYQH4Zc4qvvtlMQBHDOxCSmJshTS9OjcHoKSklAUrNgd8/oaqoZd5i/RkLh4xAIAZf62scDEHyNyZzeNjfwQgNiaSY4Z0CziP9UHLvGY0iCrlQ4dWqfTo2AyA8T/OJ7+g2Ge6d7+ZTXFxCQCnHN6rWueYMGMR6zbv4uvpC/2mWbom0/28ZdOUCttd/UPL1m4jr6CoWudvaPaFMj/tiN6Eh9vL5j0vfuv3GON/ms/7387muQ+msTFzd7XyGEpa5jWno3OV8uGgXm3dz6f/5b8PKDu3gL+Xb6Jvt1YM69cx4ONP+WM5U/5YXmW61hkp7udbtu+psN1VE/17WcU7/H3NvlDmRw3qAsD6Lbv4bf5av8fYvH0Pl47+KOC81Rct85qrcU1UREaLiPH6KRWRHBFZLiJjRCTw+n8IichUJ789fGwTEdnqbH/Hz/73ONsvr+Z5xzr79fZ4zYjI3Oq+h9omImki8piILHL+hntEZJ6I/E9E0jzSxYhIlogUiUiTKo7Z03l/U53fR3p8Vl6vYt+bPNIOq4W3GJCu7Zq6n69YX/nQ/tUbdgDQKqMRcTFRtZaHvt1accIh9qM5dfYKtmfllNueGB9Nm2Z2MMWCFZs56+g+fPXcZayfdB9ZP/+PZV/dyWv3nsX+TqBt6PaFMt+vg621/bV4Q7nXE+Ki6dQ6jWapSbWWl1DQMq+52qyJfgnMdZ6HAUnA/sBlwHkicroxpmJjdv2aAhwCDAS8hzXuD6QDpcARfvYf4jz+WCe5CzER6QL8DDQCvnN+IoEDgNuAy0TkUGPMPGNMvoh8gv37ngqMqeTQ5zqPY31sO1FEwo0xJX72Pa3676TmmqeV/WOu35JVadoNW8u2N09PYsW67UGfNyEumo6tUjnn2L5cfNJAYqIj2bk7lxsf/6JC2l6dWxAWZu+D/zPyMJITyvcltcpoxHnH9eOcY/rw0Os/NPhRog29zBsnx9G0SSIA67bsAmDEsB6MOvcQDtq/nUfedzHms5k89+F0Cov8fawbBi3zmqvNIDreGDPW+0URORb4AvhYRHobY1bU4jlragowGhtEvWtERzqP44DTRKSHMcYdaEUk3NlvrTGmemO+G65XgURgiDHmN88NInI+8A42EPZxXn4bG0TPwk8QFREBzgZygM+8Nm8BMoChwFQf+7bAlnE2kBDE+wlao6Q49/O9uQWVps3JK3Q/T0moOPgnUGcd3Ye37j+n3Gsz567mqoc+8TnRvbdHDTM5IZYZf63krS9nsXL9dhLjYzhyYBcuO/Ug4mKiuPvy4RQWFvPEO1OCzl9da+hl3iS5bC5u1t68ciNGPbXKaMQD1x7HsUO7c8pNbzbokbla5jVX5wOLjDHfAncD8c5jQ/IbkAdU/KvYILoFeMnjd0+9sAGnwdVCnWbqNdXcJwFbK//NO4ACGGPeBX4FDhCRds5rvwArgUNEJMPPoYcCrYHPjDHeS5KMdx5P9rPvqYABQt6CER1l7y+Li0soKSmtNG2+x4Ae137BaO00zXrar2MGV50xxM/I3LJJ5w+MmchRV77Mh9/9xe8L1vHjrGXc/uzXHHrJC+zakwvAvVceTfuWlba816uGXuYJcWVNmBee0J/LTjmIVRu2c95/3yXjsLtoPPQOjr76FWbNXwPAQfu34837ygeLhkbLvOZCNTr3BSAfOFVEypW+iBwmIj+IyG6nD+5XEfHZhCcifURkvIjsEJE8EZkrIlc6tR3PdGtE5GcR6eUElFwR2SgiL4hIY1c6Y0whMBPoJiLJHvvHYC/+Pzrbc6kYRCs05YpIDxF5V0TWi0ih05/4i4icWs3ych3vXqcvcJKTp7rkWkqkg4j4u828ATge8GzHeQf7OTrdzz6uT/TbPrYtBRbiP4ieBvyCvZkJKdcFpbqTTmsy7/rnv1Zy7DWvMnTks1x0zwfMmr+G5IRYrjx9MD+8ejVpjcpXxm95ajwHnf80J17/mt+m2nnLN7nn/UVEhHPV6YODzl9da+hl7tkP2LpZI1au387Qi57j88l/szs7n7yCIqbNXsFRV77ML3NXAXDMkG7VmhISalrmNReSIGqMyQX+wtZGe7teF5FLgcnYWt3H2ObEdOBTEfmv5zFE5BhsQDsM+Bp4Hpv/l539vDXHNhEmYYP4SuAaYLpT63KZCggwwOO1IUAMMNkYU4DtJzxYRKK80gD85OSvP/A7NshMBJ50HvsDn4nI8X4LyAcRuR7b1DwVGGGMya/O/tVljNkF/Am0BH4TkYtFpKlXmt+NMROMMXs9Xn4H+z94pvcxRSQSG1zX4KO51vE50EpEDvTaNwMYDHwa1BuqIVfTVWREOGFhPudYu8VEly1lll/oe4pAIGb+vYYpfyxn9qL1fPT9Xxx66YuM/WoWYOfnPTKq/Edob04Bc5du5IffllZ63A++/ZO8fFuLODTANXbrQ0Mvc+8pRLc+/RU7d+dWOGZhUQk3PT7e/fvZx/SpkKah0DKvuVDOE3WNwW8GICItscFtCdDdGHO5MeYmoAc2WD7gGjUrInHYmsxuoJcxZqQx5lZsQP4MO+DlWK/ztQNmAAcaY241xhyMDbz7AZ4LiLo6iQZ6vOaqdU52Hn/E3gAM8kgzGFhojHHVku7H1uYGGWMuNcbcYYw5nbJBNQG3MTj9j89ga2EnGGNC1cB/EZCJval5A9giIgudGvxwpx+4HGPMGmw5D3L+pp6OARoD7xj/t66fO4/etdFTsDc3n1MPPPuH4mMrH4noud3XP3iwjDGMenQcG50BHacd0ZtYjwtZoAqLSli61s7Da+VjrmlD0dDLfG9OWf5y8wsrXex83vJN7oE4B/pZj7Yh0DKvuVAGUVdpuIaDnQdEA/cYY3a4EjkB414nbxc6L58IpAGPOxdtV9pS4A7n14u8zmeA/3iN+rwbO0jlXI/XfscOevHsFz0SWGqMcY2pdgXTIwBEpC3QgvL9oU8D5xpjFnvlY6rzmE4ARORE4E3gD+BYH/2IvvY5SexUo9EiMhpoC6R4viYiN1R1HGPMfOxNxv8A14Ss7tga/PfAXBE5wMeub2MDnndt9Fzs38FXU67rnPOAFVQMoqcBvxhjqlzsVUQuF5HZIjK7OHNeVckDsm7zLvdzX4sceHJtLy0tZcuOinM5a6KwqMS9mkt0VARd2gb0MaogL9/WOKIiK9wHNRgNvcy37ihrgNm+K4fiKvoQXRf0JgF+OUB90DKvuVAutpDoPLqCQl/n8XCpOE/T1dza2yttXydIeCvxSOuy2RizzPMFY8xuEVkG9BGROGNMrjGmSER+AQY4faupzrFe8th1LrADGOb8XqE/1BgzEdzNkPsDHYCuHmkDuXq1xjZrRwAzjDGBflJPouyGw9O9Hs/XYmu3lTLGbMfemNwhIt2wzedHAcOxrQSTRaSXMcZzdv+n2Fr+mdhmbEQkETgB+NkYs6qK034O3CYi3Y0xi5z5qAcDN1WVXyfPY3BGB9fW2rmLV5V1w7Zv0YTFq7b6TdvOGayzdvMuvyu+eEtJjKV9yyY0bZLIdz9733eV53nX7wqCMdERDO7dnrRGCWzdsbfKCe2ufqZAvm6qvjT0Mt+Tk8+6zbto3ayRz4Fe3qIi7eU1a0/DHZ2rZV5zoQyibZ1H1wU1xXm8spJ9XIOAXGnPCiCti78lXFyfmmTsgCGwTbpHAV2wAVQoHyBLRWQKdk5jLLYptwSY5kojIq2B57C1ZsHOL12G7U89wHmtKo2Axdi/yygRec8YM7eqnYwxI4GRHnmZCrQ1xrQN4JyVHXexk58Xnekmn2P7ji/HI0AbY/aKyHjgHBFp7wTNk4FYfM8N9fY5dh7qycAi5zGMemrKBfjdYyH3wb3bM2HGIp/pEuOj2b+TnWryy9zVAR//jfvO5tgh3SktLaXN0fdVmGDuyXNErWs5s5ioSL553q7x8ceCdZUG0aZNEt3H8J6w3pA09DIH+H3BWlo3a0RSQgxd2zVlyWrfQSc8PIzObezaJGs9ansNjZZ5zYWkOdcZEbsfkIW9SEJZjbSDMUb8/PT1Snt4JWm9x+77u21JcR53eLw21Xnsh615lVDWV+ryIxDlpBkMzDbG7Hben2CnYZwAPAwcCCQYY7oBd/nJhy/bnPNfgw2kY0QkVH+jO0Rks4gc7Gu7U/O80fm1k48kriZbV5PuOdiblCoHBhlj/gDWYftBwU5t+dWrthtS6zbv4s9F6wE446jefptBzzuuHxERdttXU+cHfPyZzoUoLCyMC0/s7zdd0yaJHD3YLqa9ZPVW98Ula28e852vNevbvaX74uHLdWcf7F6U4dMf5gacx1Br6GUO5cvvilMHee/qdtKhPUmIiwbg62mVfz1dfdIyr7lQ9Ylejg0Kn3j0Ubo6r/p5JxaRTiLyhIicEEDaxiLyjIic57Wps+e0FSdtHLapdY4zvcVlNjZQ98U2v/5pjMnyOp6rX3QI9obAsz+0F7apc5wx5i5jzGyPwUCurxMIpCa6yRizxRjzA/AJNhhfF8B+tWE3duEDf9NNoGwkvK9+ysnY2v8pItIIOBz43Gskb2XGYZvZ9wcOpZ5G5Xp6+dNfAGjRNIVHR51YYXvnNmncedlRAKxYt41vq2iu8vTehNlkO4M6/jPyMPbrUHGabUJcNO89fL77wuC9UMKYz38F7AXq5TvP8Dno6PiD92OU8y0dC1ZsZvyUwC+A9aGhl/m3MxaxyGkCvezUgxgxrMKKobRu1ojHb7R5352dx8cT5wScx/qgZV4zdR5EReQw4B5skHrYY9N72BrfQ54T9Z15pM8DNwOu2uUXwB5sv5n3GP3HgFGA96rIUcDDrjmkzuMj2FG2b3omNMYUY0eYHontx5yMF2elpXXYvscwygdR1/STcqM+nBr4486v1R1WeROwF3hQRFpVc99gvA/sAq4VkZHeG50+zoexzdQV1hJ2Bnm9j70RuQJ70+R3QJEPrqbbV5x9vVc3Crn3J8zm5zm29+HKMwbzxdOXcOyQ7gzs2YYbzj2EqW9cR5PkeEpKSrn+0XEVJquPuedM8n5/grzfn+C848rf/23dsZc7nrPzN5MTYpnx1ijuv/oYjhjYmQE923D1GUP444ObGXJAewA+mTiH9yfMLneMN8f/5s7foN7t+OXtG7jghAPp170VRw7swst3nc5Hj15IREQ4u7PzuOTeD6ucUF/fGnqZF5eUcsX9H5ObX0h4eBjvP3IBY+45k+GDujKgZxtGnXMIP48dRbM0e/9+y5NfNuh+aNAyr6kafym3M9DnXnyvndsHu2hBHlBh7VwRuQk7EGWns/8u7LSIbsA3wMlOgENETgc+AAqxQXUTdoWd/tiRrIe5RrKKXa2nOVAE/I1dmWggdgTuFOBI77VaReQ/2ICMc6wK66OJyBvAxdig2cg1d9Npcv3VycvP2KkpqdgBPzHYWugGY0wXJ/1YbDA+wNXnKSIG+NsY09tH+XxljBnhnZ/a5jTlfoXtL16EbebOws4dPRbb73yjMeY5P/t3xy6ekItdkKGtr6ktTpB+yznWM85rYdiabAa2KXeQR/pnsDdKhxpjplb2HmrzS7nBrt351bOX0be77/uYwqJirvvf57zz9R8Vto2550zOP95Of73svo94z+viAHDNmUN4+Prj3QMifHn1s5nc8uR4nyMTkxNiePfh8zlyYBe/+6/fsovz73yPWZV8A0ZD0tDLHGBw73a8+/D5fhc/Lyou4fZnvualT372e46GRMu8av6+lLs2BxaNcH5ccrGT7F8AnvG1vqwx5ikRWYKtdZ6KHcG60vn9RVcAddJ+KiIbsCNHjwHinOM/ADzhYypILnA08BRwFbAZuA94xM9i566gmYedp+rLj9ggOtNz8QNn4NEIbE33SGxtbAN2AfcHsVNGThKRDtVcZ/c57IChE0XkVGNMnQ60McZMF/uNO6Owo3HPxo6UzsTWzp/1tSSgx/6LRGQ2ttm9srmhvvYtFZEvsH+req+FuuzcncshlzzPxScN4MzhB9CtXQYJcVFs2b6XqbNX8Oz709xNTcF48eOfmfjrEq45YyiH9u9EK+croTZl7mbGnFW89vlMn19k7LI7O58Tr3+NEcN6cO5x/ei3X2uaJMexJ6eAleu38eWUBYz5fGa5dU8buoZe5mAH1+x/+qNccdpgTjykBx1apRITHcH6LVlMnb2CVz/7pdKRrg2NlnnwalwTbYicmmiKMSalnrOiQqy2a6JKKQX+a6KhXGxBKaWU+kfRIKqUUkoFSYOoUkopFaRQrlgUMjVdqUcppZQKhNZElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWC9I/8KjT173X81efUdxaUUv8iWhNVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkg6QL0SlUhXOCILqkMadeY1o1iiQgTduYWMm/TXiYszmTT7oIaHb9RbCTDu6ayf4skMhKjiYkII7uwhNU7cvll9S5mrNpJqan8GO0ax3JUlzS6ZSTQJC6SMBGy8otYlpnDD8u2s2hLdo3yGGpa5qGnZR4cMaaKXCu1Dzl97F+1+oFOiA7nziM60jEt3uf2wuJSxvy6jmkrdwZ1/IPapnDV4DbERob7TbNiWw6PT1nFztwin9vP69uCE3qkEybi9xhTlu9gzK/rKK7qKtUAaJmHnpZ51T4d2cfnibUmqpQfAvzn0PbuC8vM1buYsmIHuYUldG2awCm9mhIfFcGVg9uwPaeQhdW8C+7RLJFRB7cjPEwoLC5l4pJtzN24h9yiEtIToxneJZXuGYl0TIvnjiM6cOeEpRSWlL84nLZ/BiN6NgVgZ04h3yzKZPm2HEoNtG8Sx3Hd08lIiubQTk0oNYZXZq6rlbKpK1rmoadlXjNaE1X/KLVZEx3WsTHXDGkLwFcLtvLu7I3ltjdPjubBY7qQGBPBul153PLlYqpz8qdP6k7LlBgKi0sZPXEZy7flVkhz2cBWHNU1DYAP/tzIF/O3urelxkfx3CndiQwPY9PufO7+dhl7CorL7R8bGcY9wzvRMdVeIO+csJRl23KqkcvQ0jIPPS3zwPirierAIqX8OGE/e+eblVfEx3M2Vdi+aXcBn8zdDEDrRrEc0DIp4GN3TounZUoMAN8uzvR5YQEY+8cGsvJs89YhHZuU2za0fSMiw+2/8Fu/b6hwYQHIKypljMdd+SEdGwecx/qgZR56WuY1o0FUKR8yEqNp3SgWgN/W7KrQvOQydcUOSpz+l4PaNgr4+N2aJrifz16/22+6ohLDkq22+axFcgwRYWU3w65jFBSXMm/THr/HWL0zj7359sLT1nlPDZGWeehpmdec9okq5UPXpmUDLCrrA8ovLmXNzlw6pMbTIyMx4OOv2J7DuHlbaBwXyZY9lY96FI+BFFHh4h40MXPNLtZn5RMRJlWOanQdwnVH3xBpmYeelnnNVSuIikhzYCRwEtAGSAEygZnA68aYHyrZNxI4HbgQ6A40BbYDPwPPGGNmeqUfCbwFLAN6GWN8/gVEJAvIMsa0rc57CYaI3AmMApqaWuhMFpGpwCFVJNttjEmp6bkCyEtnYH9jzKd1fa59QYvkGPfzzVX882/dW0iH1HhSE6KIjgijoLi0yuMv3JId0ACNcIEu6fZCl1NYTG5R2bGnrghspGTrRrEkRNt/9W05hQHtUx+0zENPy7zmAg6iInIa8CaQCMwBPgX2Am2BY4EzROQt4FJjTKnXvi2d9AOBNcBkbPDtiA3Ip4nIdcaYF32cujNwF3B3Nd5XXRkO/FAbAdTLs0CWn235tXyuCkRkf+B34GXs3+lfr3FclPv59ir+IT23N46LrPJiVB2HdkolJTYSgL837g3qGCc7oxrtMfw3h9U3LfPQ0zKvuYCCqIgcAXwC7ABOMcZM9tqeCnwIXIQNsM97bIsBvgf2A+4FHjHGFHls7wxMAZ4XkdXGmG99ZOE2EfnYGLOgOm+uNolIEnAQ8EYdHP4ZY8yaOjhuoBoBUVWm+hdJiC6bz5ZXVFJpWs878vgo//PgqisjMZpz+zZ3//71wq2VpPZtYJsUhrS3gyyy8oqYHuQ8v1DQMg89LfOaq7LhWESigNedX0d4B1AAY8x24CxgD/AfkXKzYe/ABtDXjDH3ewZQZ99l2OArwD0+sjAHiAReE5H67Fw4DHvTMake86BCJNIZ2FBSaqrshyksKbu4eA6IqImkmAhuP6KDu3lq8rLtrNjue2SjP53T4rlmSBv372//sYH8AJrg6ouWeehpmddcIEHpGGz/59fe/ZaejDE7gP9hm3xjAZxgepGT5KFK9p0E3A6M9grAAO9ga6oDgasDyG8FIvKHiOQ7tWLP1/8UESMih3u9/ozzenuPl4cD84wxmz3SNRORV0RkvYgUOo+viEizYPIZ4HvJEJGXRGSDiBSIyGoReVREKvT2i0gPEXnXI397ROQXETnVI81obPkCjHLe9zARaes8H+/juKOdbSd5vGZEZKyI/FdEspyfGz22ny4iM0Uk28nHjyJyqI9j9xORb0Rkk/M3WyYi/3NaAkLGdUGpj1nUKbER3Du8k7u/atWOXN6atb5ax+iSHs9/j+xAjLNCzKQl2/h51a5az2tt0jIPPS3zmgukOfck5/HLqhIaYx7xeqkH0ApYYoxZW8W+j1ay+QpgHvCwiIw3xmyoKi9evgP6AYOBHwFEpBHQ29l+sOt1x9HAYmPMKo/XhgOfu34RkQ7AL9gBUpOxzd29nLyeKCJDvPavMRFp7ZyzBfA1sNh5D7cCR4rIUGNMjpO2PzAVKHDyvY2yPujPROQEY8w3Tpq22AFfs7BN72uCzOLRwMnAWCAD+M3Jy/3YPu01zjaDHWQ2WUQuNMa856TrjC3LUmzf7C5gEHAbcCBQ7manLrnuZCPChDCh0rv0KI+RgP6mCASqaWIUdx3ZkYwke2HZuDufh39YUa3jHtAiiZuGtXNfWH5bs4s3qnlxqg9a5qGnZV5zgQTRjs5jMP2RLZ3HZUHs62aMWe5ciB8GXgJOrOYhvsVexA+nLFgOw9bEs7FBFAARaQt0AZ7weK0T0A6Y6HHMMdgAepkx5nWPtFc5eXyNwC/6NzijjL1lGWOe8fj9ZWwAPcEYM8HjnNdjByfdiw2oAPdjm8H7GmMWe6Q9A/gYOAf4xhgz1an8Xwj8ZowZ7aRrG2DePTUFTjTGfO1xvv7YgWFTgeOMMbnO66OxQfZVEZlojNkGXA4kA4cZY6Z4HOMb4DgR2c8YszCIfFWbZ/9QdEQYeUX+m4eiI8ouLtk+JoIHqnNaPLce1p5kZ4DFul15PDBpObvzAz/mkV1SuWRAK8Kd5rZfVu/kuelrqmyqawi0zENPy7zmAgmi6c5jhTqyiJyCrX15G2+MmYudAgN2FG9NPY7tdz1BRE6v5lSM37HTaTyD2mHYgVLfYkcHRxljCrE1ToAJHmmHA7nADAARaeXsP8MzgAIYY14WkYuAw0SkbYADhkb5eX0t8IxzzmbYpvVvPQOo4wXgFuz0I1cQfRp40zOAOqY6j+nUvjxseXq6GGd5TlcABdv8LyKPYm82zgBepKx74UDKmpjBvi9X33sFInI5NgDT58I7aT/slBq/ke3ZZSMRm8RHsSHL/yDp1Hg7JqvUGPeqK9U1sE0K1w1tS5RzoVq2LYdHJq8gu6DywR6ezu/XghN7lI1Q/GHpNl77dX29NNUFQ8s89LTMay6QIOoa5uRrmYpTgHN9vL4GmIsNUv72rRZjTLGIXAb8CjwnIj8YY7IC3LdURCYCZ4lIsjFmNzYITneOdz72wv0LtklyN3b+qstwYLrHXNXezuN0P6f8xTne/gTWNNougGDbBxuMmji1OG+FQCsRaWGM2WiMmQi2D9XJRwegKzDESV97w+vKrDfGeP839HUeTxWR4722uVoqejuPbwNXAY+KyHXYZvjvgEmuZmpfjDFjsC0DtbZ27nqPi0lGYnSlF5emifbisi27MKhmrqO6pHLJwFbub6f4c/1unpq6KuBjhQlcM6QtB3coW+rs4zmb+OzvLdXOS33SMg89LfOaCySIrsYO6umIrdG5GWPOA85z/S4iN2BrQC6uPsGOVEFE2gGbjTF+/4rGmN9F5Hlsze1x4LIA8u/yLTbgDxOR37ALPrwKTHO2Hywiv2OD6/fGmGInX1HAodgmSRfXIBd/61i5FqCMq0b+qpLiPA50fvxpDGx0+k+fwzZ9C7afcRn25uAA57XalufjtRTn8fZK9msMYIz5W0QGAv8FjsP+fS8DckTkWeCuOpij69Py7WUxu1vTBL9LlsVGhtG2sf0zL95a/e8yPKpLKpcd1Nr9++Sl23ntt3XVapa6bmhb9/D+4lLDqzPXBjxBvSHRMg89LfOaC2R0rmtAUbXbyIwxK4CVQGcRaVNF8m+AXSKyXxXp7gLWAZeISFWr/XiaiA0kh2P7QwGmGmMWYRd+OBg7DzSJ8k25g4F4yveHupqnW/g5l6vmvcPP9mC4PrkPGGOkkp/5zgjnCcAJ2H7kA4EEY0w3yt8MVMb18fb1GanOzUE2UAJEVZJn92hhY8zfxpgzsYH1UOAxbFP6f4Erq3HeGtmWXcgK5wIzuF0jv0P6D+nQxN0v8/u6rGqdo2ezRC4Z0Mr9++d/b+bVX6t3YTmzdzP3haWwuJTHf1rZIC4swdAyDz0t85oLJIh+BWwEThaRg6tI6+t4Y51HvxdvETkSWzPciB1x6pcxJhvb5CfYJryAFoxwpuC4appDsM3U853NU7HB8jhsoP3OY9fh2GZKz3zNdR4H+zndwdggtCiQvAVonvPYz9dGEblPRG53as69sCOjxxlj7jLGzDbGuGqJ3Vy7eOzu6+Ps6izx9S29HaqZ73Bs7dc7zwOd6StDnd8vEJHnRUSMMYXGmKnGmNsAV5AdWo3z1tj3i7cBtq/owgMr3i81T47mjN52NtPmPfn8WckC297iIsO4dkgbwpwL0zcLt/LRnM1V7FVep7Q4Tu6VAdh+qqenreavDQ13dZxAaJmHnpZ5zVQZgIwxeSJyPnbqwXgRudQYM84zjYhEABdQFig9h3g9CVwCXCoi64CHPfvNRKQf8L7z663eSwb6ydO3IvIRdqAR2EFDgfgWO2o1AdvH6QoeU7GDW64AfndGiroMx2uBBWPMOhGZAhwqIlcZY172eD+XYoPrT0FMxfHLGLNaRKYDx4jIacaYzzzOeT52oYppxpj/iYirSbzc4CERaYxtBgc7ctfFNUrAc9WiTOxgsv4ikm6MyXSOcQD2ZiNQY7GfjadF5BhjzB7nOInY0ca9sdNqwDZTX4UdwPWJxzHaOo+VTpOqbdNW7uSwTk3onpHI0d3SSU+MZtKS7ewtKKZLejyn9MogITqC0lLDa7+ur3Bnfc2QNgxzvtbpxZ/XlLtzPrpbOo2dgRqZewuYsWoXbRtX/c0TG7Ly3Qtzn3VAc3ftYPa63WzPKazyGMUlhg2763wlyaBpmYeelnnNBFqLmyIixwLvAp+LyHJs4NmBbdIcjr1g52KnknzosW+eU9OchA1gl4jIJOzqRj2AI52kt3sH5yqMAo7C6U8LkCuItqF83+1U5zEZj6ZcEWmKHZTzsI9jXYG92L/kjFKeB/TEvp9NOKNFa9nlzjk/FZHvsNOOugDHY2vWrsUolmNr3QeLyAzsQKdU7BzRGOzfyfNL+1zfwnuGiGQDbxtjForIm8DNwO8i8hmQhp3f+TsB1gqdz85zwPXAQhGZgJ27ejJ2DvErxpipTvLHsDczHzhTcZZjA+ipwBY8lpMMlcenrOLOIzvSMTWePi2T6dMyudz24pJSxvy6nvmbqzcA/cjOqe7n6YnRPHpC14D2u/qzBWzLLiQ9IYpezcvWn+jfJoX+bVKq3D8zu4BrPgvJLKGgaZmHnpZ58AJegN4YM1FEumIHEp2GrY2kYWsrC7E1zjecZlPvfVeISG/s6kVnYfvqmmBrkJ8ATxtjfvfer4r8ZIrILdgVkgL1F/ZinEFZ4MQYs1hEXK979ocehW3q9LXU4XKnFn0PtiwOxgbP54CHXDW32mSMWSoifbE3Ksdi+3c3Y29u7nct7uCMRh4BPIIN6n2BDdhm6gexK0udJCIdjDErjTFrReQu4AbgWmyT+kLsko052Dmk12GD2rXYgB1w06oxZpSI/IEN8ucDxcBS7LzWtz3SrRGRwc77G4L9nGx33t99xpiK3xhcx7ILSrhzwlKO6JzKkPaNaZUSQ0xEGLvyiliwOZuvF24tN8IxEInR4aQm1Gyp4vZNanPMWsOiZR56WubBkxANdlQqJGpriotSSnn6dGQfn6OuGu63xSqllFINnAZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBH1nQGlalN2XlF9Z0Ep9S+iNVGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSPotLkpVITxMOKZbOsM6NaFt41giwsPYkVPInA27GT9vCxuy8mt0/MZxkRzfoyl9WyXTLDmGmIgw9hYUs3J7LtNX7OCnZdspNZUfo2fzRI7fryndMxJJjo1gb0Ex63blMW35DiYv3U5xVQdoYLTMQ0/LPDhizL71h1aqMse8PKtWP9CJ0RE8cFwXujRN8Lm9oLiUF6avZvLS7UEdf2iHxtwwrD1xUeF+0yzdms0DE5exI6fi17wJcNXQtpzQo6nf/Vdtz+GhScvZtLsgqDyGmpZ56GmZV+27qwaIr9c1iKp/lNoMogI8OqIbPZsnATB9xQ5+WLqNnIIS9muWyJl9mpMQHUFxSSl3frOUeZv2VOv4vVsk8eDxXQkPEwqKS/lmwVb+XJ9FTmEJzZKiOW6/pu5zr9qew01fLKKguLTcMUYOaMWZfZoDsC27gE/+2szqnbkkx0QwpENjDu2UCsCGrDxGfbaQ3KKSGpZK3dIyDz0t88BoEFX/CrUZRI/skspNh3UA4LO5m3jj1/XltrdMieHJk7uTFBPJ6h25XPPJfKpz8lfP6kXrRrEUFJdy+1eLWbI1u0Kaaw9uy3H72bvvsbPW8/Ffm9zbmiVF89rZ+xMeJmzenc+1ny0gt7D8xeOsPs25cEArn/s3RFrmoadlHhh/QVQHFinlx8n7NwNgZ24h7/6+ocL2DVn5vP/HRgDaNYmjX+uUgI/drWkCrRvFAvDV/C0+LywAr/6yll25tnnr8M6p5bYd2TWN8DBxp/O+sAB8/Ncm9hYUA7ZJraHTMg89LfOa0SCqlA/Nk6Np1yQOgJ9X7qSwxPe99w9Lt1HiDGaozj9vj2aJ7ue/rdnlN11RiWHhlr0AtGoUS2RY2c3wrtwi5m7cTebeAv7asNvn/gbY6AwISU+IDjh/9UHLPPS0zGtOR+fWAhEZDdzrY1MxsAeYD7xujHkvlPmqioiMBS70szkH2AL8BNxnjNkYgvy0BVYDXxpjTqrr81Wme0bZP//8TXv9pssrKmXl9hw6pyewf4ukgI+/NDOHj/7aSJO4KDbtrnzUo2cbUmREGEXOnfjXC7by9YKtVZ4rPSEKsDWNhkzLPPS0zGtOg2jt+hKY6/F7BJAOnAG8KyJdjTF31UfGqvA2sMbrtWbAEcBlwNEi0scYE9zQvH2QqwkKYGMV//yb9xTQOT2B9MRooiPCKgyK8GXepj0BDdAIDxP3hS67oNhnU1ZlTuzZlMbx9uIyfcXOau0balrmoadlXnMaRGvXeGPMWO8XReRxYA5wu4i8ZoxZG/KcVW6sMWaq94siEg18DRwJ3AT8N8T5qjdNnH9IgG17Kx8yvz277M43NT6qyotRdRzVNY1GcZEA/Lned1OWt+SYCNo2ieP4Hk0Z0t42vS3LzGbc35trLV91Qcs89LTMa06DaAgYY5aLyHjgXGA4MKZ+cxQYY0yBiDyCDaKH13d+Qikxuuxfo6rh8vnFZdvjo/3Pg6uuZknRXDSwlfv3QC4Oo4a14+hu6eVe+35xJq/PXEd+ADWH+qRlHnpa5jWnA4tCx9Wn2ARARBJE5G4RmSsie0UkX0SWi8hjIhLvvbOIXCEi80UkV0RWicitInKBiBgRGeaVto+IjBeRHSKS55zjShHxOUS7CpnOY7neehFJFZHHRWSxk6dcEVkoIv8VkQiPdMOcPI4UkYud95AvIhtE5AkRiavs5GK96RzjbREJyWc2MtwWVUmpqXIVFc9mrcjw2slecmwE9x3bxX2R+25RJssyc6rcz9egigNaJHN093SC+eOHkpZ56GmZ15zWREOno/O40Qkyk4H+wETnJxE4EfgP0B44zbWjiDwN3ACsBF4DUoGHgHXeJxGRY4AvgEJgHDYIHg28DPQBLq9mvo92Hv/2OEcyMAtoDXwFjAfSgFOcfDUGbvE6zrVAL+Bz4Dsn7c3YvtdzKzn/E8BFwIfARcaYkNxmui4o1Z5HXQvzrhvFRvLQCV1p5fRXrdiWwys/rwlo3wkLt/LB7A2UGujSNIGTe2XQNCmaSw9qTcfUOB6bvLJac/xCScs89LTMa06DaAiISD9sgMzDBpDTgAHAQ54DjUTkNmA5cJKIxBljcp19RwG/AUcaY7KdtB8A33idJw47SGg3MMAYs8Z5/XbgY+AyERlvjPm2ivyGY4PiicD9QBHwiEeSq7CB/jJjzOse+93n5P8cKgbR3sBQY8yvTtqHnLRniMgVrvfllY+7sH2xnwMXhCqAAuQ5TVsR4WGECZXepUdHlN2V+5siEKhmSdE8eHxXmifHALB+Vx53T1gS8HFnri6bRrB4azaTl27jkRO60TEtnmGdUpmzYQ+TlmyrUR7ripZ56GmZ15w259auk0RktMfPQyLyKTADe8NyizFmG/AXcCnwjOfOxpi9zrZwbG0O4ALs6O87PQONMWYC8IPX+U/EBr/HXQHUSVsK3OH8epGPfE9xmkuNiBjs1JzNwKvO4/HGmCUe6ScCV2IDtmf+1wOrsCOSvU1zBVAn7W5gJrZcWnonFpGrgQewA5vONsYU+zhmncnz6B+Kiai8/8dz+9784LPZtWkCT52yn/vCsmZnLrd9tZisvOCPmV1QwhM/rXT/flTXtKCPVde0zENPy7zmtCZau0Y4Py5FwHZgEvCiMWYSgDFmGbBMRGJEZADQGdvc2xcY5uzr+sQe6Dz+7uN8v2AH/bj0dT06c1e9lWBrhN5cU1wEaAGche0DvQV4zni19Rhj5gBznH7dgU7eOzt57eSRd0/LfLzmGobn3cExADjBeT7JGFNxRWoPInI5TjP1fufcTqshJ1WWPCCZHiMVUxOiWLcrz2/aVGd+WqkxQc9RG9K+Mbcc3sF9t79kazb3TFjqXoWlJtbuzGNDVh4tU2LdE+sbIi3z0NMyrzkNorXrIl9TXLw5g2PuwPYJNnJezsTWzNYA3Sibe5wK5Phq7gS8F4hMcR7PquT0vpYbKTfFRUT+B/wMPIVdcOFjr/zHAA8DVwCuT+tGYDqwDdvP6c3X+HlXcPYeC5DhnL8b8JCIjDPG+F0M0xgzBmfEc22tnbt2Z9nFpFlydKUXl2ZJ9h5g696CoJq5jtsvnauHtiXMGfc1a80uHvlhRZXz8BrHRdIsOYbSUsNiP8upuexxag4RYQ13qIuWeehpmdecNufWj5uBB7GDdY4BmhljmhpjTga855DuAWJEJNLHcbyXDnF9wg43xoifnyZVZc4YswLbrynA2yKyv1eSJ4EbgW+BQ4EmxpiWxphzKKtd1sRibLncgX2PL9TCMatlSWbZP6vn0mXe4iLD6ZBqB1Mv3Ox/xRd/jtsvnWsPbue+sHy3KJP7v18W0ET2187enydO6s71w9pVmTbDuQBuz2m4K+homYeelnnNaRCtH+dgm1ZHGGO+N8ZsATudA+jqpHHdSv2JbR7tW+EottnT0zznsZ93QhFpLCLPiMh5gWTQGPMT8Dy2qfUdz2krTv4zgTOMMVONMTudc8QCbTzeS7CWOTXv17EDqk4WkRFV7FOrMvcWssy5wAzrmFpuLU9PR3RJdS+OPXOV/7VBfendIomrhrR1//7hnxt5btrqKqcauLjWGm3bOI4u6RVmRbn1b5NC4zjbFDfHz9qjDYGWeehpmdecBtH6kY8NjN6933cDbZ3nrprnW87jQ55zKkXkUOBkr/2/wNZcbxORzl7bHsOO8u1I4P6LnUbTC1t79sx/DGXNx64Rvc8CrnXEfNWcq8Xpi70ae8Pxgoj4v1WuA671OlMTorh0UOsK21umxHDugS0Au/j1rLWBX1ziosK5+bAO7gvTuL83846Pb9CozASP9USvO7idzy88bpESwyjnDr6opLTBr6CjZR56WuY1o32i9eM9YCDwi4h8gp3TeSh2HmcmdnRrEwBjzK8i8gp2NOxcEfkOaAqcCmRh+0xLnLRZInIp8AF24M8X2H7TQ7BzUv/AzrsMiDEmR0Suxc4FvVdEPjXGrHLyfwsw21mJKQK7ElMXbJ9ompP/Gn+SjTFzROQl4DpsE/iomh4zUJOXbueormn0bJ7EiT0zaJYUw4SFW9mTX0z3jATO7NuCxOgISkoNL0yveGd906HtOdIZJfjkTyuZvLRs6eETezR1D9TYsiefqct30D6AwRDrduVR7Jxo1tospi3fwSGdmtAhLZ5Xz+zFZ3M3sXJ7LmEiHNAqiRN7ZLgvOq/+spZNuytf2q2+aZmHnpZ5zWgQrR8vYZtrr8JOdcnCjl49GzuXdDxwLOCaEnItdqGFy7DBdCN2UYZmwK1AruvAxphPRWQDtj/xGOzAnzXY6SJP+Bmg5Jcx5msR+RwbtF8BjgLuBPYC52NrituARcD12MFAzzj5f6M656rEXcDpwLUi8p4x5o9aOm6VHvh+OQ8e34XO6Qkc2CaFA9uklNteVFLKC9PXMHdj1Ytsezqme9ksoIykGJ47rUdA+1343hwy95b19zz500qKSw2Hd0klNSGKKz2azVwKikt5acaaBjtX0ZuWeehpmQdPg2gtMMaMBkZXI73BDpbxN2DG3TEhIhlAoTHmCbxqkSLimqdZ7qvonfmYJwaQj5HAyADSneb1eyF2EYb7fSSfhG3WdaWdSsXRtz7P78xtrZDWGLMH3yN+69zegmJuHLeQY7qnM6xTE9o0iiMmMoyduUX8vXEP4/7eXG6EYyCSYiJIT6yd7zwsKjU88dNKfli6jWO6pdO9WQIpsZEUFpeyZU8Bs9fv5qv5W9iZW+ksoQZFyzz0tMyDJ9Ve7kmFlIjcAjwOjDTGvO3xegfsN8OsN8bsV1/5a2hqa4qLUkp5+u6qAT4rA1oTbfg+wjafjhGR47DNuhnYtWejsE29Siml6oGOzm3gjDEbsCsBvec83ojt65wEDDLGTKnH7Cml1L+a1kT3Ac7iB5fUdz6UUkqVpzVRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkg6VehqX+UqIjw+s6CUupfRGuiSimlVJA0iCqllFJB0iCqlFJKBUmDqFJKKRUkDaJKKaVUkDSIKqWUUkHSIKqUUkoFSYOoUkopFSQNokoppVSQNIgqpZRSQdIg2gCJyEgRMSIyuop0RkTWBHmOcBG5VkTifZx3qYhEV7JvVrDn9Xdu5/VEEXlMRFaISKGI7BCR8SLSO9hzKaVUXdIg+u/1AfA8EOljW2fgrlCeW0TigBnAf4BM4DngB+A44FcRGVyH+VFKqaBoEP33alrF9ttEpEcIz309sD/wnDFmkDHmFmPMWcAR2GD7ch3lRSmlgqbf4qJ8mQMcALwmIoONMaUhOOcpgAHu9nzRGDNNRKYCh4tIC2PMxhDkpZxwEYZ3S+XgDk1o3TiGiLAwduYUMnfjHr5ekMnG3fk1On7juEiO6Z7GAS2TaZYUTXREGNkFJazakcuMlTuZtmIHpabyY/Rolsgx3dPomp5AcmwE2QUlrM/KY/qKnUxZvoPiqg7QwGiZh56WeXDEmH3rD/1vICIjgbeA+4wxoytJZ4C1xpi2Hq+dia3V9cYGpXnY2t1HXvt5mmaMGeZx3huBE4FDgeuMMS94nTcLyPI8r/P6YcAdQH/sDdo84EljzGcBnPtioKkx5hEf73MCcCzQ1Riz1F95AIx4bXatfqATo8O55+jOdE6P97m9oLiUl39ey5TlO4I6/uD2jbhuaFtio/x/hduyzBwe+WEFO3OLKmwT4PJBrTl2v3S/+6/ekcujk1eyeU9BUHkMNS3z0NMyr9qXl/UTX69rEG2Agg2iIvIEcDOwBfjGSXY8kAE8Zoy5zUk3GhgJtAEeBZYYY8Z6BdEJ2CBYBHQ3xmzwOG8WXkFURC4FxgDbgC+BbGAE0B640xjzcGXnruQ9pgKrsF0PTYwxlf6H1GYQFeDB47vQo1kiAD+v2smPS7eTW1hCt4xETuudQUJ0BMWlpYz+djnzN++t1vF7NU9k9DGdCQ8TCopL+W5RJn9t2ENuYQkZiVEc3T3dfe7VO3K59cslFJaUbxQ4/8AWnNa7GQDbswv5/O/NrNmZR1JMBIPaNeKQjk0A2JiVz83jF5FXFIpGheBpmYeelnlg/AVRbc5t2IZVNULXRUSGYgPoHGC4MWab83oa8BNwq4hMMMZMN8aMFpFh2ED2P2NMlvfxjDHLReR+4GHgJWzN1N+5WwIvAEuAocaYHc7rdwKTgQdE5CtjzIJAzu3lcSAReLmqAFrbDuvcxP3P/cXfWxj7u/s+giWZOfy+Nov/ndiVpJgILh3Uihs+X0R1Ivjlg1q7Lyx3TVjKsswc97bl23KYsWoXVw5uzTHd02nXJI4Te6bz2dwt7jQZidGc3CsDgM178rlp3GJyi0rc239bk8X6Xfmcd2ALWqTEcNx+5fdviLTMQ0/LvGZ0YFHDdghwbyU/nkY6j7e4AiiA8/x259eLq3n+x7G10RNE5PRK0p0HRAP3uAKoc+48J59hwIXVPDcichf2fa0F7qzu/jV1Yk87/mlXbhEf/FmxK3bj7nw++msTAG0bx9GnVXLAx+6aHk+rRrEAfLNwa7kLi6c3fltPltO8dWinJuW2Hd6lCeFh9ub4jV/Xl7uwuHw2dzPZBcUADG7fOOD81Rct89DTMq8ZDaIN233GGPH345W2N1AK/OzjOK7X9q/OyY0xxcBlznGfE5EUP0n7Oo+Hi8hozx/gaI/8BcypBT8A7ACOM8bsqs7+NdUsKZq2jeMAmLl6F4Ulvu+9f1q2nRJnMMPg9o0CPn53584f4Pe1u/2mKyoxLNqaDUDLlFgiwsr+7Ltyi5i3aQ/bsguYu3GPz/0NsMkZEJIWHxVw/uqDlnnoaZnXnDbn/nMkAfnGmELvDcaY3SKSC8RV96DGmN9F5HlgFLZmepmPZCnO45WVHCqg20MRCQdeBS7Bzhc9yhizMOAM15JuTRPczxdU0geUV1TK6h25dEyLp1fzRL/pvC3PzOHTOZtpHB/J5j2Vj3r0vFuKCg+juNTeiX+7aBvfLtrmeycPqQn2orIrr+KAjYZEyzz0tMxrToPoP8deIE5EUrz7GUUkBojF1uqCcRdwMnCJiLznY3u289jBGLMqyHPgrJL0KXACsAYbQJcHe7yaaNkoxv18UxVD+7fsKaBjWjxpCXbYfkFx1YMa5m/eG9AAjXAR94Uuu6DYZ1NWZY7fL53Gcfbi8vPKndXaN9S0zENPy7zmNIj+c8zFzu0cQtnIXJch2Bs9zxpdwGMDjDHZInIVdsTuGCp+buYBJwH9sCNp3USkE3AFdirL1/7OLSKCXcnoBCefRxljNgWax9rWJK6sSWhbdoXKfTnbcsq2N4mPZNPu2hv/dESXJqTE2YWd5m7w3ZTlLSkmgjaNYzm2ezqD2tmmtxXbcvhy/tZay1dd0DIPPS3zmtMg+s8xFrgIeEREZnmNzn3cSfOuR3pXm0dAHQjGmG9F5CPgLOel7R6b38MO/HlIRKYbY7Y4547ALu83HFhQxbmvwy64sAIYZozxPH7IJUSX/WvkVXFXXOAxnD4+KgKonYtLRlI0F/Rv6f59fAAXh2uHtuHIrmnlXvthyTbemrWB/ABqDvVJyzz0tMxrTgcW/UMYY6YDTwE9gHkiMkZExgB/Ywf1POqkcXENw3tTRK4P8DSjgAptJU6T661AR2ChiLwpIk9ia6jDsTVjz2bgcud2mnFdKxXNA671HqDk/GQEmM8aiwy3PTQlpabKVVQ857S59qup5JgI7h7e0X2Rm7RkG8u3+R7Z6CktoeL3BvRqkcRRXVOpnZzVHS3z0NMyrzmtif6DGGNuFpG/gGuBc7E1vrnAtcaYcV7JHwK6AUdiF5x/LoDjZ4rILcCbPrY9JSJLsHNVTwXCgZXO7y86I339nXs6kOpsO8X58WU8diGJOlfqLEJiqjUjDmpj7ZKU2AjuO7YzLVPs1ICV23N4bea6gPb9bnEmH/21CWMMndMTOLFnOk0Toxk5oBXtm8Tx1JTV1XxHoaNlHnpa5jWnQbQBclbwGRtAugo3XcaY94H3A9h3BTCwuuc1xryFXdXI17ZvgW+DOTc0rJv2fKfpKiIsjDCh0rv0qPCyBp2ikpo1JWUkRjP6mE40S7YDPjZk5XHf98v9Tj3w9tuaLPfzJZk5/LR8O/cf25kOqfEc3LEJczfu4cdlwY4vq1ta5qGnZV5z2pyrlA+e/UPREZX/m0RHlm3fW1BcScrKdUmP59ERXd0XlrU787jrm2Xszgv+mNkFJTwzdbX79yO6pFaSun5pmYeelnnNaRBV+zwRuVxEZovI7DXTvVutg+M5UtE1/8wf1+TuUmPY5WPx7EAMateIB47rQkqsHaG4dGs2d36zpFbmvK3blc/GLDt9wTWxviHSMg89LfOa0+Zctc8zxozBTr2ptQXo1+3Kcz9vlhjN+l3+59BlJNlBDpl7CwNujvJ0TLc0Lh/cmjCxLdp/rMviscmrKizC7a1xXCQZSdGUlhqW+FlOzcVVc/BcCaah0TIPPS3zmtMgqpQPnmt8ds9I5Pd1vpcsi40Mo10Te9e7aEv1vt0C4OhuaVw5pI3794mLt/HKL2urHCkJ8OLpPYiLCmftzjyu/7zyRZ2aJtoL4I6cyucC1ict89DTMq85bc5VyofM7EL3UPuhHRr7vbM9rHOqe3Fsz8EOgdi/eSKXD2rt/v2TOZt46efALiwAi521Rts0jqVzmu/vgQTo1zqZRq6J7H7WHm0ItMxDT8u85jSIKuXHhIWZgO0runhgqwrbWyTHcFaf5oBdMu2PdVkBHzsuMpxRw9q5L0xfzt/C+7Ort0DTd4sy3c+vGtKGuMiKX3jcIjmaa4e2BeyIyoa+go6WeehpmdeMNufWM+ebTry/1swAecAmYArwlDFmicc+bYHVwJfGmJM8Xh+JXTmoNZCFXe6vMfAs9htc8oHrjTGeKxfV1vsYi/26swOMMXNr+/j1YcryHRzZJZX9miVy3H52Htr3izPZm19M16YJnH5AMxKiIygpNbzi4876+kPacnhnO0rw2amr+Wl52ZD743qk08QZqLF1bwHTV+ykXePYKvO0PiufYudEf6zbzYyVOxnaoTHtU+N44fT9GPf3FlbtyCVchP1bJHHcfunERdmLzuu/rmfznpB+JWu1aZmHnpZ5zWgQbTi+xC6MALaFIAkb+C4DzhOR040xE5ztWcB92C/BBkBEugFvAHuwX6Jdiv0ezqlAM+Ad7EIFf9VR/sdjF41v2N9AXE2P/LCCe4/pTKe0ePq1TqZf6/LfpVhUUsrLP6/l703V6yca3rVsCH7TxGiePLl7QPtd9uE8Mj1GVD4zdTXFpYZDOzWhSXwUl3k0m7kUFJcyZuY6Ji+t15UUA6ZlHnpa5sHTINpwjHcWOyhHRI4FvgA+FpHexpgVzre0jPZK2hsbfF80xtzl7NsMaA78YowZWWc5B4wx47GB9B9lb0EJt365mKO6pnFwx8a0ToklJjKMXblFzN+0l/Hzt7CukhGNviRGR/hctiwYxaWGZ6au5sdl2xneNY1uTRNIjo2gsKSUrXsK+WvDbiYszGRnkFMS6oOWeehpmQdPTG2s36SC5tGce5GvIOqkuRV4FHjHGHOhnzQjsSsJ3WiMecZ5rS0+mn3/yWpriotSSnn68rJ+Pkdd6cCifcML2P7MU0UkQkTaiogRkfEAIrKGsqX4nna2jcUGUIARzmtTXelFJMv7JCIyzEn3jMdrESJyr4jME5EcEdkpIhNF5HCvfcc6+/b2ev1MEfnF2TdbRGaKyFl4ceVZRAaJyFQn7S4R+di5GVBKqQZHg+g+wBiTi+3LjMc223p7BtunCjAR2186HjugCGCp89rYIE7/PLbpeCc2mH8CDAAmisiwynYUkSeAj4D22O8K/RBoB3woIo/62KUvdiBVCbZfdx5wBvCj800vSinVoGif6L7D9fVhzSj/XZ4YY55xapYjgO89mnPnYr++bIkxZnR1TygiScDlwHRjzDCP118H/gCuwQ5c8rXvUOw3uMwBhnt9v+lPwK0iMsHr69l6ALcaYx530grwPXAUcKjzXCmlGgytie47XGO2k0J4zjDst6u08vwuT2PMbKADcE4l+450Hm9xBVBn323A7c6vF3vtk0dZ7RljO+y/c35tW/3sK6VU3dIguu9IdB6zQ3VCZxTwx9gm2HUiMkVEbhWR7saYVcaYyobC9cZOs/nZxzbXa/t7vb7WGOO9XpdrHTJtzlVKNTgaRPcdbZ3HVSE+7wXALcAyYBh2lPBCEfnDexCRlyQg30dQxBizG8gFvL9qwdcMaddo24a7irdS6l9Lg+g+QEQaA/thF1lYVAuHNPj+21f4/iBjTJEx5kljTA+gDXApMAnoB3wjIpF+zrEXiBORFO8NIhIDxAIN85uKlVIqQBpE9w2XYweBfWKMKakqcQAKgVhn4I6nDp6/iEg7EXlYRI4HMMasM8a8YYwZjh0c1ALb1OvLXOdxiI9tQ7A1y8q/kkEppRo4DaINnIgcBtyD7Qt9uJYOuwQblI/2OE9j7GhbT3nAbcADnlNMRCQKO0q4AP/L/I11Hh9xRuS69k0DHnd+rfU1fJVSKpR0ikvDcZLHogKutXP7AEOxwewsY8zaWjrXa8CJwCci8j62ZnoasALo4kpkjNniLLxwE7BARCZgBwsdDXQDHjDG+PzOIWPMdBF5ytl3noh87Ww6HhuAH/Wa3qKUUvscDaINxwjnxyUXu6D7C8AzxpiVtXUiY8w3InIucCt2Kso24G3s9JLNXslvBZZjF8Ifif3MLARGGmPeruI8N4vIX8C1wLlAEbaZ91pjzLhaejtKKVVvdO1c9Y+ia+cqpeqCrp2rlFJK1TINokoppVSQNIgqpZRSQdIgqpRSSgVJg6hSSikVJA2iSimlVJB0iov6R9myp0g/0EqpWpeRFKlTXJRSSqnapEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCFFHfGVCqvhUXF/H1uM+YPHECq1euoKi4iLS0dPoOOIjTzjqPNm3b1/vxS0pK+P6b8fz0w/esWrGcPbuzSEpKput+PRlx6pkMHDy02vkqKSnhmkvOY/HC+fTu049nXx0bxLsLjpa5lrkv+2KZ61ehqX+U6n4V2u6sLG4ddSVLFi3wuT0qOpqbb7+Ho48fEVR+auP4mVu3cMdN17Ji2RK/aY4bcQq3/Hc0YWGBNy598M6bvPr8UwAhvaBrmWuZ+9LQy9zfV6FpTVT9a5WWlnL3rTe4//GHHTGcY44/iYSEBOb9PYf333qN7Oy9PPbgvaRnNKNPv/4hP/7evXsYdcVINm3cAMCAQUM5/qRTaZKaxsrlS3n3zTFkbt3ChC/Hkd40g5GXXR1Q3tatWc1bY16s1vupDVrmWub/tDLXmqj6R6lOTfTbr77g0QfuBuCs80Zy1ahbym1fu2YV1156Pnt276Zdh068+cHn1boDro3jP/nIfXw17lMAzr7gYq687qZy23fu2M4l557Gzh3biYqOZty3P5GYlFxpvkpLS7nusgtYMG+u+7VQ1Yq0zOe6X9MyL7MvlLl+KbdSXj754B0AGjduwsVXXFthe5u27d13vKtXLmfWzBkhPX7m1i18M/5zAPY/oF+FCwtA4yapnHfRZQAUFhQw8+dpVebrs4/eY8G8uSQkJBIREdrGKC1zLfN/WplrEFX/ShvWrWX1yuUAHHL4UUTHxPhMd8zxJxEeHg7A1MmTQnr8KT98T2lpKQCXXzPK77kOOexIjjr2BE4/5wLS0jMqz9f6dbz+0nMAXHn9zURGRgb2hmqBlrmWua/j7+tlrkG0lonIaBExzs9dVaR9ziNt2yDPd7aIVBj2JiK9RGSsiKwVkQIR2SEiM0TkOhGJ8krb1snD+GDy4HWsTiLyhIgsEJG9IpIjIn+JyB0iEu+RroWIlIjINhGp9DZRRE5w8je2pvlzmf/3HPfz3n0P9JsuLj6ejp27APDX7FkhPf6smT8DkN40gx77H+D3GKlp6dx53yNce+OtlfZnGWN47IF7KCjI54B+/Tnh5NMCei+1Rctcy9zX8ff1MtcgWrdO8bdBRKSy7YEQkUeBD4Akr9fPAP4CTgZmAE8DnwPpwHPATBHx3CcLuA/4qIb5uRpYANwIrAdeA94GYoGHgd9FJA3AGLMR+BFIBQ6r4tDnOo9ja5I/T2vXrHI/b9mqdaVpm7doBdhmp7y83JAdf9XKZQB06bZfufS5OTmsW7Oa7dsyA8qLyxeffsjfc2YTExPLf+4cXa19a4OW+ehq7VsbtMxHV2vfYOjo3LqzBThARNoaY9b42H4Q0ALIBhKCPEdT7xec4PgasAYYaIzZ7rEtHHgZuAwbNG8EMMZkAaODzIPr2OcBLwIrgBOMMUs8toUDDwG3AV8DA51NbwNHAmcBPtuQRCQROBFYDVTdERKg7Zlb3c+bZjSrNG1607Kmo+2ZmbRq07bOj787K4tdO3cCkNGsOQDTf/qBj99/u9xAifSmGZx02lmcfs4FREWVa2AoZ/PGDYx54WkALr7yWlq0rPyCVxe0zLXMvY//TyhzrYnWnfHO48l+tp8G7MbWFGvTwdia6XueARTAGFOCDZxF1LAW7ElEUoAXgELgKM8A6jqvMeZ2YCYwQESOdjZ9AewFTvZuYvZwMrYm+46pxaHke/bscT+PjYuvJCXExMa6n2dn7w3J8Xdn7XK/lpCYyJOP3Mfdt91Y7sIC9q5+zIvPcONVF7N3z26fxzfG8NiD95KXl0f3Hr04/ezzA3oPtU3LPPS0zOueBtG68xO2mdRfsDoV+AobeIDK+yY9+lpPcn5fA1zobJ7j/A7g6kHv6eukxpgc4CTg4qrOKyIdRORDEdkqItki8q2IdBORFSIy1SPpaUAy8L4xZrWf9wu2JnoVsMjJSy7wGZACDPezzzmAAd6p5LjVVlRoiz08PLzKkXvR0WWDJQoLCytJWXvH92zu+varL/hq3Kc0b9GSex9+gm9+msnEGX/w9Etv0L3n/gAsmDeXB++53efxv/7iU/6aPYvIyEhuvev+ak1fqE1a5qGnZV73NIjWnSJskBwkIuWaXUWkP9Aa+LQGx38G+Nt5/qrzO9iabT5wioh85QzKKXeLaIz51hjzY2UHF5GOwK/AGcDP2Gbg9s7zJl7Jj3EeJ1Z2TGPMz8aYV4wx6zxeftt5PNNHHpoCRwAzjDGrvLfXRFi466Pvc+qXX7Yru+6Pn5+f735t65bNtGjZilfGfsRhRx5NYmISMTGx9DlwAM++8hY9e/cB4LdfZvDbL9PLHS9zy2Zefu5JAM676DLadehYrfzUJi3z0NMyr3saROvW59gy9l7r6jRgD376AQNhjHkGmOv8+orzO04T7mXYIH4CNpDvEpGZInK/iPgf/lbe00AacKYx5lRjzH+A/bG1yBSvtC2dx2VBvJXp2P7bE0XEe3z8mUA4tTigyCU2Ng6AkpJiSkpKKk1bUFD2j15Zf0xtHj86OrpcmmtuvI3klJQK+0ZFRTHqlv+6f5/03Tfltj/+0Ghyc3Jo37GTe55dfdEyDz0t87qnQbRuTcIOHPJu0j0V+MoYU1AXJzXGvAf0wdbydmObeA8C7gb+EpHxIpLqb39n27HYGuBnHsctwDbJektxHgPrSCmfVwO8CyQCx3ltPhfIoYoau4hcLiKzRWT2u2+9HtB54+LLKuf5eXmVpvXcnlTFKim1dfw4j/6l6OgYBgwa7Hf/Tl26kpZuGzsWL5jnfn3CV+P4/bdfCA8P59a77iciInTzE33RMg89LfO6p6Nz65AxJl9EJmCbVpONMbtFpA+2WfTGOj73AmCkMwfzQOBwbLPrIGzNuKmIDPIzWKcv9gbrdx/bZgHFXq/tcB4bBZndd7AB/kxs7R0R6QD0B941xmRXtrMxZgwwBgJf9q9pRnP388ytW2iX4L/5J3PrFpw80SQ1LZDD1/j4jVPL7nFSGjWq8sKQ3jSDbZlb2Z2VBcCO7dt56enHARg0dBgREZEsX1pxYe/SUltceXm57u1JyclVjrQMhpa5pWX+zypzDaJ173NscDgeeB/blLuXKvoPa4sxphjbt/kr8KCIHAR8iZ1mMgyY4mM31yd7i4/jlYiI98StVdiabkfgD395EZFooLn34CNjzAoRmQkcJyLxzuCnWp8b6qld+w7u55s2rK+0D2XTxvUAZDRr4XdFlto+fkJCIk0zmrF1y2b27t3jd1+XoqIiABKT7PTfdWtXuUdAzpj6IzOmVtoFztLFi7j0PDsp/ejjRnDH6IeqPGd1aZmXp2X+zyhzbc6te98CeZQ16Z4KfO2nKddVi/L1d4kL5GROU+1mf1NGjDG/Yvs7ATr5OYzr05zkZ3ui1+/fO49HVZG9E4FVIvKuj21vY9/jic7v5wBr8R3ka6xbj17u5/Pm/uk3XU52NiuWLQWg1wF9Qnr87s4xcnNyWLNqpd9jFBcXs36tvS9xzbVriLTMQ0/LvO5pEK1jTq1qInC0iBwIdMZ/H59rXLmvCVcdfLzmq+myEMigbMSsz2w5j5v8bP/LSVNhbS0R6U7FIPo1sBM4x9cShM5+4cB1zq8/+EjyCWWjinsCXajluaGemjVv4V4h5cdJ3/kd0v/9hC/dAyaGDjs8pMc/7KiyP+H4z/wvJjV9ymTynP6mIYfYYxzQtz/T/lhQ5U+sM3evd59+7tfqokYEWuZa5v/MMtcgGhqfY2tZz2EHGn3vJ10msAvoLyLprhedEbXeg27AjsAF8Kx1ur487xUnaJcjIp2A67EB1OfoYGdJvh+AI0XkWI99o4HHfKTfDdzp5OM7Eenidc4Y4HlgKDAHu1Sh9zGysCOJh2NXMIKy6S914pQzzgFgW+ZWXnrm8Qrb165ZxdjXXgKgRavWHDTkkJAef9DQQ2jbzt47ffn5x0z/qeK9x+ZNG3nhqUcBiI9P4Iijj62QpiHRMg89LfO6pX2iofE1toY4EPjQGJPvK5HT3/gmcDN2ndnPsNNMTscO8hnqtctG5/FJEZlsjLnPGDNNRG4GHgd+E5EZ2JplIdAVW0MtAI42xlQ2o/p64DfgK2cRhg3Y5lrXiINy49mNMa+ISHPsAKGFIjIROx2mCXZQU2tgCTDC6af15W3svNQbgJ+NMf7bdmrB8ONOZMJX45g350+++PRDNm3cwIhTzyQ5OZkF8+by7luvkb13D2FhYdx0290VJpM/MvpOvp/wJQC33/Mgx5xwUq0ePyIiktvvfZBRV1xEQUE+995xM0cdcwKHHjmchMQkFs6by/tvv+Fe9eW6m2+nUWPvKbwNi5Z56GmZ1y0NoiHgjMr9ERvAPqsi+R3YaR0XYps/lwPXYptLvYPoi8Bg5/XuIvKkMSbbGPOUiEwBrgYOwY62jcAuCv8K8LgxZkMVeV4qIoOBR7ALHkRiV2E6E5gHVFih2hhzj4h8C1yDHRF8CHYW9mInr88bYyobBz8RO5gpgzoaUORJRHjwsWf5z/VXsHTxQmbNnFHhuw4jIiK46Y576DfgoHo5frf9evL4868y+o6b2bljO99P+NJ9QXMJD4/g6htuqXBxa4i0zENPy7xuaRCtZcaY0fhYzN0Y47P9wRhzktfvRcC9zo838Uq7HT+DeYwxc7CLLlTJWSDffWwRCcNOw1lujCm3UISItHOervdzrN+wNdhqc9b2rf1x/pVITknhpTff55vxnzF54resWbWCvNxcmqSmcUC/AZx57oW07+hv/FVojr//AX1577Nv+OLTD/l52k9sWL+WwoJCmmY044B+/Tn59LPrdVWc6tIyDz0t87ojdTRuQ+3DnK9p24NtLu7l2ewrIi9h1789wxhTk2UL60Sg80SVUqo6MpIifa5tqDVRVYExxojIK8AtwDwR+Q7bBzoY2687kaqbpZVS6h9Pa6LKJ6dJ9yJsk3AXbJ/oKuyCEU85zc4NjtZElVJ1wV9NVIOo+kfRIKqUqgv+gqjOE1VKKaWCpEFUKaWUCpIGUaWUUipIGkSVUkqpIGkQVUoppYKkQVQppZQKkgZRpZRSKkgaRJVSSqkgaRBVSimlgqRBVCmllAqSBlGllFIqSBpElVJKqSBpEFVKKaWCpEFUKaWUCpJ+Kbf6Rxk7e119Z+FfpXVKdH1n4V9nv7Tk+s7Cv1JGUqTP17UmqpRSSgVJg6hSSikVJA2iSimlVJA0iCqllFJB0iCqlFJKBUmDqFJKKRUkDaJK/b+9+w6PqsweOP49CRBK6L1XaYp0RCyIiIg0e9e1LeraV1flZ0PddV11XXvBXQS7WAAFQQQEFEWlI9J7B+mEkHp+f7x3kskwaZPJTDKcz/PwZGbuO/eevMCcuW81xpgQWRI1xhhjQmRJ1BhjjAmRJVFjjDEmRJZEAREZISIa8CdTRJJEZLWIjBSRttGOMxgRmenFe1KQYyIiO73j7+by/se848MKed3R3vs6+b2mIrKosL+DMcaUVrZ2bk4TgEXe4zigCtAR+DNwjYhcqqqTohRbbr4DegM9gd8CjnUE6gCZwDm5vP907+f0YonOGGNimCXRnMar6ujAF0XkfGAc8ImIdFLVNRGPLHffASNwSfS/Acf6eT+/AC4RkZNUNSvRiki8976Nqro2ArEaY0xMsSRaAKr6tYg8CvwLeBT4U5RD8jcXSAZODXKsH7ADeB24xHvuf7d6MlAZ+LSYYyw0EZkJNFPVZlEOhcyMdFZ+P5l1v85k37aNZGakU7FaTRq07Uz7s4dSrV7jsF4vIz2NL5++i/3bNzLwby9Qp0X+PQmH/tjBshnj2bZ8IUl7d6EKlarXokHbTrTvewFV6zQMa4zFLSM9nfkzJvLbnBns2ryBjPQ0KteoRYsOXel53kXUatgkzNdL463ht7J7ywZuevIVGp3QPs/yX458noXfTS7Que9++QOq1a4XjjCjLj09nWmTvmDOjClsWr+W9PR0atSqzcldTuH8i66kYZNm4b1eWhoP3nY1mzes4+8vv0Pr9h3Cev5wsD7RgnsVOApcLCI5vnyIyNki8q2IHPD6UX8SkUuCnUREuojIeBHZIyLJIrJIRG4VEQkot0FEfhCRk71+zyMislVEXhWRGr5yqpoK/Ai0E5Gqfu8vD5yBa6b9EThC9p2pzzFNuSJykoi8JyKbRSRVRA6KyBwRubiQ9eU73+NeX+lUL6ZS5ejhg0x67n7mfvIGu9YtJ+3oETLSUjm0ezsrv/+aL5++k9U/TQvrNeePH83+7RsLXH79/O8Z/9RtLP/uSw7s2Ex6agoZaSkc3LWVFbMnMeGpv7BmbulprT9y6ACjRtzF5HdeYfOqZaQkJ5Gelsq+nduYP+0r3hp+C4tmfRPWa0776L/s3rKhwOV3bChJjVGRcejAfh69+0ZGvfIsK5ctIflIEmmpKezctoVvJ37OA7dezcypE8N6zQ//9yqbN6wL6znDze5EC0hVj4jIAqAX0AmYByAiNwMjgd3AJ8BhYCjwqYg8rKpP+84hIgNwzcKpuCbWXcB5wBtAFyBwcE8DYCawAZfEewK3A2eJSE9VPeyVmwn0BU4BpnqvnQ6UB6apaoqI/ACcKSLlvMTrKwMww4uvh3euFOBz73dqBVwAfCYig1W1wP9LROQuXFPzTGCoqh4t6HtLAs3MZMbIv/PHxlUANOtyOiec2o+yFSqxa+0ylkwZS2pyEnPef4nEGrWp36Zjka+5ZMonLJs+rsDld29Yyex3niMzIx2Ji6PtmYNodGJX4uLLsH3lYn6b/gUZ6Wn88O5/qFC1Og3bdSlyjMVJMzMZ+8IItq1dCUD7U3rT6az+lK+YyKYVS/l+woekHEniq7f/TdVadWh+YuciX/P78R8y9+vPClw+MyODXV7C7dznfHqcOzTP8pWr1yxKeCVCZmYmzz/xAGtX/g5AzzPPoc95g6lYKZEVvy1i3IfvcCTpMG/9+ylq1anHSZ26Ffma4z58h4mffVDk8xQ3S6KFs9X7WR9ARBrhktsK4AxV3eO9/jAwDXhKRL5U1d9EpCIwBjgAnKKqG7yyD+GS759FZLyqfu13vebAl8BFqprhlX8ZuBO4H5egwPWLgkuyviTqu+v03SZNB87FfQmY6b12GrBMVXd4z58EygJdVXW5LwgRucyL8SqgQElURK4FXgTmAINVNbkg7ytJ1sydxs7VrvX7pHMupvvFN2Udq9uyPY1P7snXz99PStIh5o59kwsefg2JC61xJyM9jZ/HvsXK77/Ov7CfBV++S2ZGOgC9b3yQ5l3PyDrWoF1nGrbvypQXh6Oaya+f/5eGj7weUnyRsmj2VDauWALAqYMu49yrb8k61rj1ibTp2otRI+4i+fAhpox5jVufGVmkOp885jXmT/uqUO/bvXUjGWlpALTo0IV6zVqFdP3SZNbUiSxfsgCAwZdey7W33J11rM2JHel2am8evfsmDh86wDuvPsdzIz8iLsS/l/S0NN557Xm+nfh5WGIvbtacWzgp3s8q3s9rgATgMV8CBfASxuO4+vX1nw4BagPP+RKoVzYTGO49vSHgegr8zZdAPY/i7nav9nvtFyCJnP2i/YCVqrrFe+5LpucAiEgzoCE5R+X+B7jaP4F6Zno/61AAIjIEGAX8Cpzvd8ec13su8KYajRCREUAzoJr/ayJyT0GuHy6/eXeEFapUp/Pga445Xq1eYzoNdH8N+7dtZMuyeSFdZ/eGlXz9/P1ZCbSgSSEjLY3tK13CqdWsdY4E6lOvdQeadHL/LPZt3cDhPTtDijFSfHeElapWp8+l1x9zvFbDJvS+2P2X2rV5PasX/RLSdbauWcGox+/OSqCFScQ7NmaPwavXNPYTKJB1R1i1ek0uv/6WY443bNKMS6/7MwCbN6xl0S8/hnSdNSt+49G7b8pKoHFx8SFGHDl2J1o4lb2fvqTQ1fvZN8g8zUTvZ6eAsl29JBEow6+sz3ZVXeX/gqoeEJFVQBcRqaiqR1Q1TUTmAKd4fau1vHP533YsAvYAZ3nPj+kPVdVvAESkHm56TEugrV/ZgvyLboK7ay0DfK+qBwvwHnBNxsEGbD3u93gj7u622B3YtZX921y/ZNPOp1GmXELQcieceg6/fDYSzcxkw4IfaNyhR6GuM2/cOyz99jNQBaBJx55Urt2AZdO+yPe9KUcOoZnu+1VeA4eqN2jKxoVzADhyYB+JNesWKsZI2bN9C7s2rweg/SlnUjaXOu/Uuz/fvPc6mpnJ7z/PonWXnoW6zrSP3mbOV59k1Xmbbr2oUbchP00q2Pi6HRtWA1A2oTw165WuAVuh2L5lE5s3uC8OPc88m3IJwYc2nNV/MGPe+A+ZmRn8NHsaXXqeHrRcbj54+xW+HPsu6v29dOvVm3oNGzPx0/eL9gsUM0uihdPM++nr6a7m/bw1j/f4BgH5yl5RgLI+W4OWciNuAariBgyBa9I9F2iDS6BCzgSZKSLfAUNEpAKuKTcDmOUrIyJNgJdxd82Cm1+6CvgB6Oy9lp/qwHLcv627ReR9VV2U35tU9Xrger9YZhLF0bm71v6e9bh+65NzLVe2fEVqNGrBnk1r2L5ycaGvs3v9ClAloVJlul14I61P68/CiQX70CifWBWJi0czMziwc0uu5Q7u3p71uGK1wH9iJcfmVdkDx5u1z71/OaFCReo1bcX29atYv2xRoa+zZfVyUKVCYmXOuWoYXfqcz8zPxhT4/Tu8hFKvacuQm5JLk5XLsv9dn9ixa67lKlSsRLNWrVm3ajm/Lfy10NdZvXwpqkpi5apcM+xOzh5wAWPHvBVSzJFkSbSAvBGxJwL7Ad8nrO+OtKWq5jeEzFe2r6rOKOBlK+TyejXv5x6/12Z6P7sBZ+IS5HfkNB031aUbLonOU9UD4FY3AiYB7YGngfG4/tJkEakL3FzAmHcDZwMdcP2zI71BUJkFfH+JsH/H5qzHVeo0yLNs5dr12bNpDUn7dpOWcpSyuXxTD6ZcxUQ6nHspHc69hIRKlfN/g5+4+HgandSdzUvm8sfG1Wxc9CNNO/XKUWbP5rVsWPA94Jp8E2sUqEU+KnZv3ZT1uEa9RnmWrVG3AdvXr+Lgnl2kHk2mXPnc/qscq0KlRE4bcgWnDb6CComFq3OAnZtcEq3btCUr5/3IwlmT2bJ6OcmHD1GxclWatDmRbv2GhGXQU0mwZdP6rMf18plaVLd+I9atWs6e3Ts5mpxM+QoF/3upVLkKQy//E0Ov+BOJlavk/4YSwpJowQ3D1ddYvz7KJbhmyG5k350CICInALcAs1T1K68sXtkZAWVrAI/hkpr/bUhrEanqS3Re2Yq4ptaFfqNswY0WPoxrNj4dmK+q+wN+B1+/6Om4LwTP+B07GTgJ+ExVHwl4Xzvf5cnfNm+g0g4RGQtchhsI9VIB3ltiHNmf/f2kUvXaeZb1P35k/x9UrZt3AvB39rCHi3Q30/2iG/ljw0qSD+7ju7f/Sfs+g2nQrgvxZcqyc+0ylk79jMz0dBIqVeG0q+8K+TqRcGjvH1mPq9bMO9lXqZld54f2/UHN+gWfq3vZvSNCrvMDf+wk+fAhAJb+MJ15336Z4/jh/Xv4/efZ/P7zbLr0HcjAG+4mLr7k9+vlZe8fu7Me16qT93zXmnWyuwr27tlFg0ZNC3yd+x5/NuTBSNFkSbQARORsXJI7jLtL83kfeBj4h4jM9o1y9eaRvgL0J3txg3G4/rwHvVG4/n2dzwI3AU8EXLoc8LSI3KGq6t0t/hOohBu4k0VV00Xke9yAorZeOQLKrBGRTbi+xzhyDiryTT/J8enlJfjnvKdlA8+Zj78CA4C/i8gXqro5vzeUFClJh7Iely1fMc+yZctl33mmJicV6jpFbQ6sWrcRgx54gQVfvsvaX2eybPp4lk0fn6NM086n0f2im6hcq2RP+E/2q/OECvnUud/d/tGkyNX5dr/5oSnJSdRt2pLu/YZQp3Fz0tNS2fD7Yn75ZjxHkw6xYPokBGHQzfeGfL2SIOlQ1nd4KlTM+++lvF+LwJHDh/IoeazSmEDBkmigC7xRq5C9dm4X3KIFycAVqpo1C15VV4vIA8C/gWUiMgHYh0sc7XDTQd73yu735pR+CCwUkXHANty6tz1wI1mfD4gnDddP2FlE5uKmsJyKa6Z9I0j83+ESMmTfdQaaBtyIS5r+Q+hW40b5nukl4zm4AUoX4OabHgEKNeFNVbd6g6j+jZsKlPeEupzvPasw1wq3zHQ3hUHi4vK9k4gvVy7rsW/qQyTtWrecfds3ZQ2UCbRzzTI2LJzDSX0vLNF9eL66K0id+w86Sk9PzaNkePn6QwE69xnAoJvuzRFr8xM70/msAYx+8l4O/LGT+dMnclKvPjRr3yliMYZbmvf3EhcXT3x83imjnN/fS1oU/i9EgyXRnIaS84P+CNkLHbwYbH1ZVX1BRFYA9wEX40awrvWev6aq6X5lPxWRLbgpLQOAit75nwKeDzIV5AhuMYYXgNuA7bi71X8GTHvx8fWBJpMzQfqbjkuiP/ovfuANPBqKu4Pth2sW3gJMBv6Oa/q9QERaFnKd3ZdxXwSGiMjFqloqJn9lJ5uCtGD7vU8KV76o5o8fzZJvxgJQp0U7Og+6htotXOv77vUrWPz1R+xYvZR5X/yP/ds3cvo195TYROqLq7B1KIX8OyqK0wZfRrvup3Fgzy5adewRNNlXq12XIcPu472nHwBg7uTPS3USjcv6eync+yL59xJNlkQBVR1B9sIFobz/a6BAs+RV9Sfc6NeCnnsuboGEgpSdRz6f+qr6Ie5uONixHRw7V9XnwoCy1+M3mtZ77Zhre18ich/eWkKVSXDNUpqZQWZmRp7z1TJSs++E4ssWtsU7dJsWz81KoA3bd+WcvzxOnN+dQoO2najXugOz33me9fNmseanadRu1oa2Zw6MWIyF4RsclJmRf52npaZkPS5Ttlyu5cKtbEJ56jZtSd2mLfMs16JDV6rVqc/+XdtZv2wRqhrxL1jhUt5rWs/IyCAzIyPPVoJUv7+XsuUi9/cSTSXzK6kxUVY2IbtvJz0lJY+SkJaavZphQsXCj/YM1fJZbvEokTh6XX1njgTqExcXz6lX3p7Vr7t8ZnjXNg2nBL/+tLSjea8QmZaSfTyUEbaRUK9JCwBSk49wNKlw/YMlSXm//umjR/NeeMz/eGkaYVsUlkRNqSciw0RknojM+2Xix2E5Z6Lf6NCkfbvzKOl3XIQKVSM3D9O3pm/1hs3ynLqSUDGR+m1cY8D+7RtJy+eDMFqq1s4e2XlgT951fnBPdp0nltC1af0HP2Wkp+dRsmSrXbd+1uM9u/Ne8WrPLndcRKhes1axxlVSWBI1pZ6qjlTVbqrarcegvNayKLhq9bPnwx3yW6wgGN/xxBp1c13ZqDike3dj5Som5lMSyleulvU47eiR3AtGUe1GzbIe79u5Lc+ye73j1WrXy3Vlo3DTzEzWLZ3P0jkzWP7rD/mWTzqwD3B9vSX1brkgGjdtkfV457bcF/UA2LndHa9dt0GuKxvFGkuiJZSqNlPVatGO43hVu3n2Hp471yzLtVxq8hH2bnFThOu2OrHY4/JXPtHtfHfoj7yTPGTPexWJIyGxZDazNWrVLuvxxpVLcy2XciSJHRvdVJOmbQJX2yw+EhfH2Bef4ItX/8HXo17KWp4umPS0VLauczvR1G3SgvgykesrD7dW7bLrePnSRbmWO5J0mA1rXOtI2w6dijmqksOSqDFBVK5Zl1pNTwBg3byZuU5dWTN3GprpFmNq2inYvujFx5e0k/buZseq3JPOkQN7cyxUX1I/0KvVrkeDFm0A+O3HGaSnBZ+6smj21Kw6b9u9cOuzFlXTtq5Z/PD+vaxdkvuGAwtnTiHliJu/etKpfSISW3GpU68BLVu7TcrnfPcNaanB/15mTZ1IpreWc4/TS/fvXBiWRI3JRdvegwF3F/fL528fc3z/js0smuR2t6hcuwGNO5wS4fgGZT2e88FLJO3/45gy6alHmf3Oc2SkucFR7c4aHLH4QtHd25vz0N4/mPr+m8cc/2PrJmZ97ta5rVGvIa27RPaLS/d+2QPrp4x5lSMHDxxTZsua5Uz7cCQAidVq0LXvoGPKlDb9L7gMgL1/7OLdt/5zzPGtmzbw6bvu/0i9ho3pWsjF50uz43qKi4hcD7xTgKIXqur4Yo6lPHC7qv67OK9T3EQktzauDOAgbkH7D3FzaDP83jeCnDu25KZzQRa0D4dWPfuy+sdv2LlmGStmTeTQHztoe+ZAEipVZte65SyZ/AmpyYfd6Ngrbz9m6P/3Y15gzVy35sXp193LCaf2C3aZkNU74STanTWY5TO/4uCubUz4xx206z2Yuq1OpEy5BPZuXseyGeM5uMvtY9CkUy9adD8rrDGEW8czz2XhzMlsWrGUX6dOYN+u7XQ7ZzAVK1dl86plfD/+A44muTofeOOxS+qNf+NfLJ7tttQdeuvf6NT7vLDG16pTDzqc1pelc6azZ/sW3hp+C70GX07Dlm1ITTnK6oVz+XXql2SkpxEXX4ahtz5A+Ur591mXdL37DeS7yRNYvnQh30z4lF3bt9Jv8CVUrlKVlcuWMO7DUSQdPoTExXHzXQ8dsyjDa8+OYNZUNzL8L397nLP6l+wvc4VxXCdRP7PIXsA9mBURiqENbnWf0u4Ax25ZVg5ogVsB6RTcik63BXnvBNy2bbnZkcexsBIRzr7lUb599VH+2LiarcvmsTVgz9C4+DKcetUdNGgXncXGe1w6jLj4MiybPo6Uwwez7owDtezRh15X31Xi5yqKCJf/9Qk+eGY429atZM2iX1gTsGdoXHwZBt10Dy065L6jSHEacsv9iAhLfpjGwb27mTLm1WPKVEiszNBbHqBVx+5RiDD8RIT7RzzH08PvYu2q31n4y48sDNgzNL5MGf5893BO7hrZFplosyTqzPQWXIimkrnJY2j251af3r6rvwC3iMiLqroyoMh4VR1dzPEVWPnEKgz82wus+mEK636dyb7tG0lPOUqFqtWp36YjJ/W9iOoNm0Utvri4eHpc8mdannI2K2ZPYseqJRzZvwdVpUKV6tRt2Z7Wp59HvRM6RC3GwqpYuSo3PfkK82dM4rc509m1ZSOpR5OpXL0GzU/szKkDL6VO4+ZRi69M2XJcePtwOvU+j/kzJrJ51e8kHdhH2YQEqtWuR5uuvejebwiVqlaPWozFoXLVavz9lVFMnzSeH2ZMYfPGdRxNPkL1GrU4qXN3Bl1yNU2aHx+blPuTvEaYxTq/5twnop1ERWQDUK20j8j1mnM35rUPqIiMAa4D/qKqb3ivjcA1595QlCT6zIy1x+8/6ChoUi1yU3qMc2LtqtEO4bjUsXHloM04NrCokMS5VUQWiEiyiOwTkS9F5Jj2PBFJFJFHRWSRiBwSkaMislpEnhWRSl6ZZl7iaQpUFREVkdHesZne82oB523mvT7e77XR3mvdReR371o/eju/ICKtROR9EdkpIikislxEhotI2YBzJ4rIf0RkhXeOXSLyhYh0CWM17vJ+2iewMaZUs+bcwhsDXAssA97EbUt2GfCjiAz0bbjtbYc2DbdDyzfen8q4dXP/husfvAS3yfcTwD243VKeIe8+wfx8hWsunQoc9rZQ64Lbw7QC8AWwEbczzdO4XVsG+Q3yGYtbHH8ibmPuesDlQH8R6RKk+bVQRCQOONd7urgo5zLGmGizJOqc5TUnBjNaVTcAiMiluAT6IfAn3w4tIvJP3KbY74pIC2+z7EtwA2j+4b/JtYg8iNt27AIRqehtnD3Ca1quFoZm5TmqerHf9QSX+BOAXqo63+/YC8C9uM3DX/f6KwcA76rqn/zKTQQ+BW7GfQEoNG/0cUvcDjYnA7NV9bsgRf23owv0YpCNxo0xJmosiTq9vT/BzMRtVwZu42yAewK2OFsvIm/gNujuB0wCFuCSzgT/k6nqIRFZgEtWNXDbnYVT4FZjpwAn4aaUzA849ihwO27nltfJbt5vIyJVVPWg93w87s55UwFjaJrHVBf1zjcsl+OB29H5G427czfGmBLBkqhT0IFFXXGbWd8eZKqAb524TsAkVV0FrBKR8iJyCtAaaOWd4yyvbN47D4dmfZCYAVrmcrd9COjo3bEuBX7Cbfy9Q0Rm4vYT/UpVA8+bF/8pLmVwW7n1Adbh5twuyeO9RRpYZIwxkWRJtHCq4eosr0UBakBW399w3ObcvrHuu3CbZW/AzZMsjkl7gVt0VPN+nuf9yU2id5d8LvAAcDXubnkA8LKITAP+7GvazscxU1xE5A7gFeBzETnD27vUGGNKNRudWziHgc2qKnn8uc8rex/wd9zgmQFAfVWtq6oX4gb2FISvSTTw76liYMF8Yga4KZ+4DwGo6mFVfUxVW+IWf7gTmAucA3xSiOvm/EVUX8X1zbYCPhWR4rgLN8aYiLIkWjhLgEYiUi/wgIgMFJG/i0hH76WrcEvdDVXVKb47L6/Z1Nf0638nGqwP0bfSc6WA11sWMmaAbkFiLisi/xaRO73nHUXkORHpCaCqq7zkdzpuMFQPESnKdvV3AZu9892XT1ljjCnxLIkWzmhc4nvVP5mISH3cdJfhuD5GcH2n8UDtgHM8CjTzHvvP0UwLeA7Zyw1mrWDtjXItzAjZ2bh+0ptEJHC17oeAv5Ldb5oA3A88Kjk7favgmqR3eCOPQ+INVLrde/q4iERv2RljjAkD6xMtnNG4eZ4XA0tF5BtcHV4G1AQeUtV1Xtn3gZ7AHBEZi7ur7AN0wfWN1vHe47MVOEFE3gemquq7wChc0nnJuzv8Azdy9QCQVJCAVTVDRK4DpgCzRWQCsBZ3Z3o2LsEO98r+IiKfe7/fAhGZgUvsFwC1yB6dHDJV/UpEvgAuAt4g735aY4wp0exOtBDUrZF4CXA3bmrKzbiFCH7HjTr9l1/x13H9iXu8clfh7lKvJHt6x/l+5R/ELeDgm4uKqi72yszDJeprcAs49AXSKSBV/QG36MOnuEUW7satkPQycKqq+u/qfC0uqZbx4rwel3SHqOqogl4zH3fidnTpLyJXh+mcxhgTccf12rkm9tjauZFla+dGnq2dGx22dq4xxhgTZpZEjTHGmBBZEjXGGGNCZEnUGGOMCZElUWOMMSZENk/UxBQbLWpi3bLdB6IdwnGpY+PKQV+3O1FjjDEmRJZEjTHGmBBZEjXGGGNCZEnUGGOMCZElUWOMMSZElkSNMcaYEFkSNcYYY0JkSdQYY4wJkSVRY4wxJkSWRI0xxpgQWRI1xhhjQmRJ1BhjjAmRJVFjjDEmRLaLizH5yEhPZ/6Mifw2Zwa7Nm8gIz2NyjVq0aJDV3qedxG1GjYJ8/XSeGv4rezesoGbnnyFRie0z7P8lyOfZ+F3kwt07rtf/oBqteuFI8xiZXUeeVbnobEkakwejhw6wAf/Gs62tStzvL5v5zbm79zG4lnfMPCme+jUu3/Yrjnto/+ye8uGApffsWFN2K5dElidR57VeegsiRqTC83MZOwLI7I+WNqf0ptOZ/WnfMVENq1YyvcTPiTlSBJfvf1vqtaqQ/MTOxf5mt+P/5C5X39W4PKZGRns8j6IOvc5nx7nDs2zfOXqNYsSXrGzOo88q/OisSRqTC4WzZ7KxhVLADh10GWce/UtWccatz6RNl17MWrEXSQfPsSUMa9x6zMjkbjQhhlkpKcxecxrzJ/2VaHet3vrRjLS0gBo0aEL9Zq1Cun6JYXVeeRZnReNDSwyJhe+b8qVqlanz6XXH3O8VsMm9L74TwDs2rye1Yt+Cek6W9esYNTjd2d9sBTmA2rHxrVZj+s1LTkfLKGyOo88q/OisSRqTBB7tm9h1+b1ALQ/5UzKlksIWq5T7/5ZHwa//zyr0NeZ9tHb/PexO9i2zjWltenWi54DLi7w+3dsWA1A2YTy1KzXsNDXL0msziPP6rzoLIkaE8TmVb9lPW7WvmOu5RIqVMz6Zrx+2aJCX2fL6uWgSoXEygwedh9X3PcU5cpXKPD7d2xw39DrNW0ZchNbSWF1HnlW50VnfaIxQkTqASOAgUBdYC8wDXhMVdcFlL0OuBdoDewDxnrlDudy7m+BA6p6ife8K/AocAZQGdgMfAo8papJAe+tCAwHrgQaAuuB14DXVVWL/IsXk91bN2U9rlGvUZ5la9RtwPb1qzi4ZxepR5ML9eFQoVIipw25gtMGX0GFxMqFjnPnJvfhUrdpS1bO+5GFsyazZfVykg8fomLlqjRpcyLd+g0Jy2CQ4mZ1HnlW50VnSTQGeAn0F6Ax8C3wMdAGuAoYICI9VXW1V3Y48DSwBHgF6IBLqD1F5CxVTQ04d0VcsrzTe94HmOId/hzYBpwJPAicLSJnqupRr2w8LrmeD3wNfAYMAF4FmgP3h70ywuTQ3j+yHletWSfPslVq1s5+374/qFm/cYGvc9m9I0L+Zn3gj50kHz4EwNIfpjPv2y9zHD+8fw+//zyb33+eTZe+Axl4w93ExceHdK1IsDqPPKvzorMkGhtG4BLofar6gu9FEbkGeA/4NzBERJoCTwI/Ab1VNc0r9yTuznIYLsH56w0kAFO956/jugFOU9VfvPcL8BbwZ+AvgC+Gy3EJ9HlV/ZtX9lFcEv6riIxR1aVhqoOwSk46lPU4oULFPMuWTSif9fhoUlIeJY9VlKap7X7z5lKSk6jbtCXd+w2hTuPmpKelsuH3xfzyzXiOJh1iwfRJCMKgm+8N+XrFzeo88qzOi65kNS6bUF0I7AZe9H9RVd8H1gL9RSQOlyTLAE/7EqjnaeAgcHOQc/cHVqrqRhFpD7QFJvgSqHcdxSVncHeaPrcD6d75fWXTgEcAAW7K7RcSERWR0bn/ysXLN5xe4uLy/VbrPxgjPT01j5Lh5esnAujcZwDD/vEGXfsOonHrE2l+Ymf6XHo9t/zzLarWqgvA/OkT2fD7oojFV1hW55FndV50didaynlNpk8DaaqaGaRIClAOKItrdgWY6V9AVY+KyE+4ZFtVVQ/4He4PfOM9Pohrtv2NY6V4PxO9uBKAHsAiVd0XUPYX4AjuLrdE8n1zdjfZhXgfhStfFKcNvox23U/jwJ5dtOrYI+iHYLXadRky7D7ee/oBAOZO/pxm7TtFLMbCsDqPPKvzorMkWsqpagbwUrBjItIWd+e4VlVTRKQlsDOXAUQbvJ+tgV+99zfx3n+/d60twLO5hHKh93OZ97Mp7t/X2sCCqpohIpu9a5VIvkETmRkZZGZmEBeX+7f0tNSUrMdlypYr9th8yiaUp27TltRt2jLPci06dKVanfrs37Wd9csWoaqF/tCMBKvzyLM6Lzprzo1RXvPtq7i/45HeyzWB/bm8xXf3WdXvtf5AKgF3rkGuVZfs5lz/a5HP9SqKSIn8IpfgN/Iw7ejRPMumpWQfD2XkYSTUa9ICgNTkIxz16wcrSazOI8/qvOhK5AeYKRq/gT59gXlk95WWJbvZNZDv9fJ+r/UHfgicthJwrarAJNy0mpf9+krLBpw3r+sdFpF7gGoBZTqJyAi/54tUdXyQGIbh+nu56eFnOPuiq3MLt8Cq1q6b9fjAnt3UqVgp17IH9+z2BUJiCV0n1X9QSEZ6ehQjyZ3VeeRZnRedJdEY493ZvQ1cD6wDhvpNW0nG9Y8G4xs1kOSdJx6XhJ/J41q1cSNtuwATgfv8Did7P/O6nuL6RgHuwTUB++vo/fEZA4wPPJGqjsS7A/5wwZawzD2t3ahZ1uN9O7dRp3GzXMvu3bkNgGq16+W64ku4aWYm65ctJOngAcqUK0e77qfnWT7pgOuWlri4EnsXYXUeeVbnRWdJNIZ4czp98zJXA+eo6ja/IvvI2Vzrz/e6r1n3FNyd4TfBCnv9q98ALYEvgctU1f+rn28wUV7XO+wbDKWqzQLOr8AYVb0+l/cXq0at2mU93rhyKW269QpaLuVIEjs2uiH4TducFJHYwH1IjH3xCVKOJJFYrQZtu52Wa/9PeloqW73l1uo2aUF8mbJBy0Wb1XnkWZ0XnfWJxggRqQ7MwCXQhcDpqropoNgqoK6IBFtqpDmQiUu+4JpydwKLg1yrE/AjLoGOAS5W1cBm2w24/tTmQd4fj5vXujLwWElRrXY9GrRoA8BvP84gPS34kP5Fs6eimW5QdNt8viWHW9O2JwNweP9e1i6Zl2u5hTOnkHLEtcifdGqfiMQWCqvzyLM6LzpLojFARMrjmlNPAWYBZ6nqriBFf8D9nZ8R5P09gWWq6uuN7w98G7g0n4i0wi28UAe3qMINAXegAHiv/Qx0FpHAdpUeQEXcog8lVndvz8JDe/9g6vtvHnP8j62bmPX5GABq1GtI6y6nRja+fkOyHk8Z8ypHDh44psyWNcuZ9qEb65VYrQZd+w6KWHyhsDqPPKvzorHm3NjwNNALl5QGqGpyLuU+BP4PGCEis/zuHv8PqILXrygiNYDuBKxe5I34/QioDbykqv59oMG8i0vYTwB/9c5RFnjKO/52QX/BaOh45rksnDmZTSuW8uvUCezbtZ1u5wymYuWqbF61jO/Hf8DRpMOIxDHwxmOXGhv/xr9YPNst9DT01r/Rqfd5YY2vVacedDitL0vnTGfP9i28NfwWeg2+nIYt25CacpTVC+fy69QvyUhPIy6+DENvfYDylRLDGkO4WZ1HntV50VgSLeW8dXNv954uBx7Mpc/gGVVdISLP4xZMWCgiXwEn4hatn0N2UjsHt6LQ1IBzXAB0w42sPRwwctZnh6r6vs6+A9wA3CsiHYD5wHm4wULP57Xkn6pGfVKdiHD5X5/gg2eGs23dStYs+oU1AXspxsWXYdBN99CiQ9eoxDjklvsREZb8MI2De3czZUzgqo1uOsLQWx6gVcfuUYiwcKzOI8/qvGgsiZZ+PckeAXtjHuVeBI7idlTZjFvj9m5gB/Af4Am/O9P+wOIgTcK+FY8SgIdzuc5i4E3IWlThPNyd6GXA6bjFF+4A3ijA7xZ1FStX5aYnX2H+jEn8Nmc6u7ZsJPVoMpWr16D5iZ05deCl1Gl8TLdvxJQpW44Lbx9Op97nMX/GRDav+p2kA/som5BAtdr1aNO1F937DaFS1epRi7GwrM4jz+o8dFKCd6MyptDCNcXFGGP8XdWlUdDWMRtYZIwxxoTIkqgxxhgTIkuixhhjTIgsiRpjjDEhsiRqjDHGhMiSqDHGGBMiS6LGGGNMiCyJGmOMMSGyJGqMMcaEyJKoMcYYEyJLosYYY0yILIkaY4wxIbIkaowxxoTIkqgxxhgTIkuixhhjTIgsiRpjjDEhsiRqjDHGhMiSqDHGGBMiUdVox2BM2BxOsX/QxpjwS0wQCfa63YkaY4wxIbIkaowxxoTIkqgxxhgTIkuixhhjTIgsiRpjjDEhsiRqjDHGhMiSqDHGGBMiS6LGGGNMiCyJGmOMMSGyJGqMMcaEyJKoMcYYEyJLosYYY0yILIkaY4wxISoT7QCMiba0tDTGfT6WKV9PZM2a1aSnpVG7Tl1O6dmLK6+5jubNW5TI87/w/DN88O5oAOYvWZFn2ZSUFMZ9Ppap30xm7ZrVpKakULtOXbp07caFF19Kx05dQoohVFbnVucFVdLr3LZCMzGlsFuh7d+/j7v+Moxlvy0NejwhIYHhj4xg8NALQ4qnuM6/ZPFCbvrT1WRmZgJ5f7hs3bKFu++4hfXr1uZa5tLLr+KB4Y8QF1f8jVNW547Vef5KUp3nthWaJVETUwqTRDMzM7nlputYMH8eAOecex5Dhl5EYuVEFi1YwKj/vcXhQ4eIL1OG1978L9179CxULMV1/tTUVK689AI2rF+X9VpuHy7JR45w5WUXsnnTRgBOP7M3g4ZcSO3adVi/bi3vjv4vmza6Y3+68c/cdc99hfodC8vq3Oq8tNa5JVFzXChMEv1y/Oc88djDAFz7pxu5574Hchxfv34dN113JQcOHKBlqxP4+LMJhfoGW1znf/nFfzNm1Ns5Xsvtw+Wt119h5JuvAXDNdTdw7/0P5jiekpLCdVddyprVq4gvU4YJk6ZSv36DAv+OhWV1bnVeWuvcNuU2JsD7Xj9LzZq1uPX2u4453rx5C4bddgcAa9esZs4Ps6N+/t+XLeX9MaMAqFa9er7lJ4z73MVQqzZ33H3vMccTEhK4xYshIz2d76Z9m+85i8Lq3Oo81urckqg5Lm3auIG1a1YD0LffuZQvXz5oucFDLyQ+Ph6AaVOnRPX8aWmpjHj0/8jIyOC88wdxUoeT8yx/5EgS3bqfQpu27TirT1/Kli0XtFzzFi2zHu/YsS3PcxaF1Xk2q/PclbY6tyRagojI9SKiufw5KiJbRWSciBSu0yL8cd7jxXR9NOMoikWLFmQ97tqtR67lKlVKpHWbtgD8+svPUT3/22+9wdo1q6levQb3P/hwvjFUrFiJJ5/+Fx+OHcf/PToi13Lbt2V/oNSqVSff84bK6jyb1XnuSludWxItmWYBTwT8eRPYBFwAfC8ip0ctuhiwYV32YIUmTZvlWbZR4yYA7NyxneQjR6Jy/hXLf2e01z/0t4cepnoBmrgK4ujRo/x35OsAxMfHc3a/c8Ny3mCszh2r89iqc5snWjLNVNURwQ6IyJPAo8CzQK9IBhVLdu3amfW4Xv36eZatW7dejvc1bdY8oudPS0vjicf+j4z0dM7s3Yf+Awbme/28pKWlsXPnDn6Z+xPvjxnFxo0bALjtjrtp1Khxkc6dF6tzq/O8zl9a69ySaOnzd+Ah4FQRqaiqBfvKaHI4eOBA1uOKFSvlWbZChQpZjw8dOhTx87/zv5GsWrmCxMqVGf7IiAJdPzeZmZmcfkoX0tPTsl6rUqUq9z0wnEFDLijSufNjdW51ntf5S2udW3NuKaOqqYDvX245ABEpKyJ3i8hcETkgIqkislFE3hSR2v7v9/oyR4tILxGZKSKHRWSfiHwiIs0CryciQ0XkJxFJEpHNIvIwEB8sNhFpLSLvi8hOL4a1IvKsiFQNKDdaRNJFpKaIvC0iu0XkkIh8IyItRSRBRP4lIttE5KCIfCciHcNRfz6pqamAa9opUybv75IJfoMl0rz3Rer8q1etZNTbbwJw971/o07dugW6fm7+2L07xwcLwKFDB5n01QQWLphfpHPnx+o8m9V57NS53YmWMiLSFagFbFLV/d7LHwEXAz8AI4HywLnALUBXoHvAaboC33nlXwdOAS4DuolIe1VN8a51M/A2sAt4D6gEPEx2EveP6xRgOlAB+ApYB5wK/A0YLCKnqepe/7d4McQDo4GTvZgnAmuADsCnQH3gUmCSiLQO1513XHycL/DCvbGA5cNx/vT0dJ547P9IS0ujW/dTuOiSywp3riDKlCnDv55/kXr1G3DwwAFmfjed8eM+45eff2Lhgnn887n/0Ofsc4p8nWCszq3Og52/tNe5JdFSQEQEqIrrA33Ze/kJ71hPXAL9QFWv8XtPGWABLjG2VtVVfqc8CXhAVZ/zO/8UXBLrA0wRkWrA88AW4FRV3eKVfQnIMdFLROJxSTYBGKiqU/yOPQM8CDwH3OT3tjjgCNDbL2nP8X7HBKCDqh7yXn8HuB7oDUwuRNXlytf0lJGeTkZGRtbw+2BSjh7NepyQEHz4fHGc/93R/2P578soX74Cj4x4qkDXzU+NmjU559zzsp73Ov0M+vQ9h3vuuI20tDRGPDqcrt26U6VK1TzOEhqrc8fqPLbq3JpzS6bH/ae3AJnAPmAS7i70PlUd5ZXdgkswj/mfQFXTcXeaAIHjuZOBl/zKKtnJqZn383xc4n7Jl0C9svOAMQHn6wWcAHzkn0B9vwuwFbhaRBICjr3hS6CeH72fI30J1OMbE9+MIERkmIjME5F5o/47MliRY1SsWDHrcXJycp5l/Y9XqVotIudfv24tb3srsNx2x1009kY2FodTe53OlVdfC8DhQ4eY/u3UYrmO1Xk2q3N3/lioc7sTLZlmATO9x1VwzZmNgPeBYaqa9a/RS3BjRKSMiHQB2gAtgc6Ar70i8OvhRq9v1Z+vidaX6Hx9kPOCxPcjcKvf807ez2OWIlHVFBH5FTc1py2w2O/wmoDiSd7P9QGv+77CBiZh3zVG4pqxC7zsX/0GDbMe79yxncRWJ+RadufOHQCICLVq1c61XDjPP+LR/yM1NZUGDRrStVsPVq5Yfsz7kg4nZT32HS9btiwtWrYqUIz+zj6nH+95K8SsXr2y0O8vCKvznKzOY6POLYmWTDmmuIjIo8DXwDW4ZHeHf2ERuQV3J+pbDHI/MBdYjuvvDOywSOFYvuTjK+uboBVsmN7egOdVvJ/H9JV6fLOcKwa8nhRYMI/4wqql33/ALZs30zKP//xbNm8CoEGDhrmuyBLu8/+21H3X2LZtK9dccXG+17vqMrc7Rv0GDZg4ZQbgVn7ZumULW7Zspk3bdtSunfsE86p+dx7paWm5lisKq/OcrM5jo86tObcUUNUk3MCfncDtXtIEQEQuxS3E8Afubq+JqlZX1QHAwiJcdp/3M1inQWLAc1+ibRhY0ONLyHuKEE9Y+S8ltnBBsJtt5/Dhw6xa6Ra97tSla4k5f0HM+m4GFw89n7tvv4XJk77Ks+yWzZuzHtfxm88XTlbnOVmdh0e069ySaCmhqjuB27ynL/hNR7nK91NVJ6jqZr+3tfN+FnLoHAC+ceCnBTnWLeD5Iu/nMasoiUic9/phYGMIcRSLBg0b0f7EkwD4ZvKkrKH6gSZ+OY6MjAwA+vTtF7Hzz1+yIt8/p5/Z+5jyvm/nAB07dc7aLWPihPFZezIGM2HcZ1mPT+lZPGt4WJ3nZHUeG3VuSbQUUdVxwBe4ZtE3vJd9/YU5JlaJyHW40awAZUO43NfAbuAuEWntd962wM0BZX/A9W9eJCLnBxx7AmgMjA0YRBR1l11xNeBWT/nP888cc3z9+nWMfONVABo3acoZZ55Vos6fn9p16nJWn74ArF27mv+OfCNouQ/fG8P0aW6QRdfuPehwclin5OZgde5YnYdPtOvc+kRLn7twA4bOE5ErcYONrgDGichHwEGgBy6B7sKNzK1Z2Iuo6mER+TPwGfCLiPi+wl2KS67V/MpmisifgG+Ar0TkK2AtbtRuT1zf7N8K/6sWr0FDLmDCuM9ZuGAeYz/+kK1btnDJZVdQtVo1Fi9ayKi33+LQoYPExcUx/JHHj5lM/vgjDzHxy/Hu8VNPM2ToRWE9fzj89YHhLFq4gL179/DW66+weNEChgy9iAYNG7Fr5w4mfjme2bO+A9xWViOefDrsMfizOrc6j7U6tyRayqjqVm/VoFeAF3EjXq/AzcW8Bjf3ch1wO/ATbq7o+bgFGQp7rQki0hcYAVzunXsk8CvwSUDZH0WkO26A0znAebjm278D/1LVw4W9fnETEZ5/8RXuvO3P/L7sN+b8MPuYvQ7LlCnL/z06IqSmn+I+f0HUr9+A10eO4v5772TL5k3M/XEOc3+cc0y5li1P4Ln/vEyDho2KJQ4fq/NsVufhE806tyRagqjqaNzqPfmVexV41e+lTwhIan5y9IeqatD+0dyuraqzgbODvGVskLIryO6jzZWqXo+b2xr4+ghcwi5QbOFQrVp13nnvY8Z9/ilTvp7IurVrOHLkCLVq16Z7j1O45robaHVC6/xPFKXzF8QJrdvw8WcTGP/Fp0yb+g1r16wmOTmZKlWq0LZde/r1P4/zBw0tljuEYKzOrc6LQ7TqXLRg0+qMKRUKOk/UGGMKIzEh+NqGNrDIGGOMCZElUWOMMSZElkSNMcaYEFkSNcYYY0JkSdQYY4wJkSVRY4wxJkSWRI0xxpgQWRI1xhhjQmRJ1BhjjAmRJVFjjDEmRJZEjTHGmBBZEjXGGGNCZEnUGGOMCZElUWOMMSZElkSNMcaYEFkSNcYYY0JkSdQYY4wJkSVRY4wxJkSiqtGOwZjjnogMU9WR0Y7jeGJ1HnmxWOd2J2pMyTAs2gEch6zOIy/m6tySqDHGGBMiS6LGGGNMiCyJGlMyxFQ/USlhdR55MVfnNrDIGGOMCZHdiRpjjDEhsiRqjDEmKkSkYrRjKCpLosYYY8JKRNaJyF35lHkM2BCZiIpPmWgHYMzxRkQ+Br5X1deiHUssEpFRIb5VVfWmsAZznBCRZkAVv5eaAW1F5ORc3lIOOAeoVLyRFT8bWGRMhIlIEvCOqt4R7VhikYhkBnnZ90EnuRwTXBKNL7bAYpiIXAF8SHY9F+htwFRVPa94oooMuxM1JvJ2k/NbuwmvzgHPawAfAHuBJ4EfvceJQHfgcaAaMCRyIcYWVf1YRDoDdXDJ8TpgMbAoWHEgDdgKlPrWGLsTNSbCRGQA8DHwFvAFsB5IDlZWVQ9GMLSYJCL/A/oBnVV1T5DjVYCFwHxVvSzS8cUiEVkP/EdVX452LMXNkqgxEeZ9wNQk//4gVVVrLSoiEdkLfKCqd+ZR5kXgBlWtGrHATEyw/6DGRN5GYmBUYimiuObavDQCjhZ/KMcPESkL9MENMkogeH80pf1u1e5EjTExTUQmAP2B/qo6K8jxi4CxwEeqem2k44tFItIUmAa08L2US9FSP5jLkqgxUSYi9YEaqrpMRMqoanq0Y4olItIBmAOUByYD84FDQFXgNOBsYCfQU1U3RSvOWOJN47oMmIqr8wPkMnJXVcdEMLSwsyRqTBSISAXcqNDrgdp4/Z8i8iDuruk2VV0ZxRBjioh0Al4Czgg4pMAU4A5VXR/puGKV1w+9WFX7RDuW4mZ9osZEmIgkAjOBLsBmYB3ZzV4VgbOA70Wkh6puiEKIMUdVFwG9RaQBcDJQHdgHLFTVndGMLUaVBX6OdhCRYMv+GRN5j+AS6F24QRcf+A6o6uPAn3BzGx+NRnCxTFW34eYvLlHVKcAxU15MWMwHukY7iEiwJGpM5F0GTFHVV9X1p+ToU1HV94CJuJGNJgxEpIKIPCMiO4AtuEQKcJ+IzBCRNlEMLxYNB84Qkb+KSEy3eMb0L2dMCdUAt9hCXlbi+kZNEVnzeVT8GVgFPAc8KSIbgZQg5VRVS/Udq92JGhN5u4F2+ZQ5yStnis6azyPvety/YcF9UWkHdMrlT6lmSdSYyPsKGCwiQRfeFpGLgQHA1xGNKnZZ83mEqWpcAf+U6jmiYEnUmGh4AtgOTBSRL4FzAURkhIhMxE383wU8Fb0QY0oDgi+E7m8lUL/4QzGxxvpEjYkwVd0pIr2AN4GBZK/m8pj3czYwTFW3RiO+GGTN51EiIu1xzeWdgOqq2kNEBuGaz99X1WDb1pUqlkSNiQJV3QwMFJF6uP66asBh3NSLDVEMLRZ9BQwTkfO8aS05+DWfj4x4ZDFMRB7Ctab4mmx9zei9gb8CF4nIpaqaFo34wsVWLDImSrxVdFJUdbnfa8NxGxXPj1pgMUZE6gLzcM21X+N20OmJ+4Dvhkugu4GudvcfHt4Xk09xe7c+jqvje1U1XkRaAa/itqf7q6q+FL1Ii86SqDER5s2b+y9wLfCEqj7pvV4eSPKKvaiq90UpxJgjIo1xzefncexi6L7m81URDyxGiciPuOUsT1LVFBF5HHjMN5BIROKBJUCqqgZuol6qWHOuMZF3F3Ad8A3wpd/rKcCZwIPAPSKysbRvE1VSWPN5xJ0MvKmqweaGoqoZIjIZGBbZsMLPkqgxkXcj8KuqDvB/0Zt+MUdEhuKWTbsVsCQaRqq6A5s6FAnpQGI+ZaoDGRGIpVhZEjUm8prj+oSCUlUVkenAnZELKXaJyGP5lyIDOIJb0egHL9ma0P0KDBWRh1R1f+BBr596KK6vulSzJGpM5O0F2uZTphluD0ZTdCPIHhkabHPowGNpIvKYqv6ruAOLYf8EvsUtp/g4UBeyNuvuDvwDdyf676hFGCY2sMiYCBOR/+KWRbtYVScEOX4uMAn4RFWviXB4MUdETgQm4DbhfgG3Qfc2oApulO7fcEvT3YFrgrwd6AxcoqrjohFzLBCRm4FXgHK+l8j+wpIJPKCq/4lGbOFkSdSYCPO+jS/Afah/C8wFDgKVgR64hecPAj1UdW204owVIvImcDHQWVW3BDneCPf38ZGq3u1tmL4M2Hg8bCpdnESkIW4Ueo7BXLiFFtZEMbSwsSRqTBSIyEnA68DpQQ7/BNyiqr9FNqrYJCK7gI9V9a48yrwJXKSqdbznLwNXq2rNCIVpSinrEzUmCrwEeab3Tf1kXP/QYWCpqq6PanCxpxxQIZ8y8bgmXZ8jZDdDmnyIyMnADlXd5fe8QFR1SbEFFgF2J2qMiWkiMhPoAHRX1XVBjjfDTSlaqaq9vNdmAHVU9aQIhlpqiUgmMMJv4ZBMAnbLyU1p38nF7kSNiQIRqYnrp2sGJJDLqFFbtSgsnsQtbPGLiLyCm36xAzewqAdu8YuquBGjiMj/cOu7PhmVaEunMeTcKeddCphESzu7EzUmwrymru9wAy2CJU8fLe3f0ksKEbkI1wddh5wf7oKbcnS7qn4iIg2ALcDPwLmqeijiwZpSxZKoMREmIpNwC3K/jVs95wC5fGtX1VkRDC2miUhF3Nq53YBauBHQ84EJqnrEK1MZaA0sUPtwDJmIjALGq+qXeZS5Fjd4K+jm9KWFJVFjIkxEDgLfqerQaMdiTHHw+kQfV9VcN5YXkfdwc3HzG/RVolmfqDGRlwmsiHYQxxMRSQDOwDXnxpPdjC5AWdz2aANU9ayoBFjKichfgUcCXh4uIvfm8payuNHQy4o1sAiwJGpM5H2P263FRICINMf1QTfOp2hmBMKJVa8Bl+O+pIAbqJVC8KUrFUgDtuJ2LCrVrDnXmAgTkba4zYrfA561jaCLl4i8D1yF63+eBdyPG0k6HWgPXIkbXNTRN8/RFE3glJdYZknUmAjz5iA2we3mAnAU9609kNqKOUUnItuArara3Xv+AVBfVc/2nvfFTYF5RFWfiV6kscNb2nK/qsb8JgrWnGtM5LXANWltinYgx4mawId+zxcDg3xPVHW6iHwDXAJYEg0DVd0IICLlgRrk3g99vqo+HpUgw8SSqDERpqrNoh3DcSaJnPNx1wGJItLcb4nFJcCfIx5ZjPKmE40GLsAl0LyU6iQaF+0AjDGmmC0G+oiIL5EuxyXVbn5l6pP/h70puMdxd/Z7gCm4LosVuGbzjbj634VLsqWa3YkaEwU25SKi3sHdFU0XkXuApcAa4FkROQTUw40snRetAGPQhbiVn9qpapKIfAWkqurFACLyGC7Rlo9ijGFhA4uMibDCTLlQ1bIRCCnmicjzwD3ANar6sbcM4Fiyv7yk4760zIhSiDFFRJKB/6nqHd7zEbjt/er7lZkHHPQN8CqtrDnXmMh7Cjc6dzLwEPAHMA0Yjpv2ko5r6moYrQBjjarej6vzqd7zL4BewEvAv3AboFsCDZ80wH/d4TVAHRGp4/fad7glFks1a841JvLOBuar6iAAEemEm3LxrPf8PVzf0Y3YaNEiE5GPge9V9TX/11X1F+CX6EQV89bi9sn1WYW76+8IfOu9Vg63CUOpZneixkReTdykf5/FQFffE1Wdjkuil0Q4rlg1GGgX7SCOM18A/UXkCRGpgfs3vg94UEQqeV0alwGlfgN6S6LGRF6uUy78XluC22vUFN1u3N6hJnJewA3UegQYqqop3mtn45LpGqAu8EbUIgwTa841JvKyplx42235T7nwfTO3KRfhcxvwsYg8i7tDWg8kByuoqgcjGVis8kbknoZrTVngvfw0kIpbZvEo8L6qvh6lEMPGRucaE2Eich1uysVM3IjRpcBK3NSW23BTLt4A5qnqGVEJMoaIyHpcE3qlfIqqqtqNhSkU+wdjTISp6rsicjIugbZX1SUi8hBuysUkr1g6pXwllxJkI7Ah2kEcD0SkMdAf96VlKzBZVfdEN6riZXeixkSJiDQAjqrqXu95D+AKXFPXJ6q6OJrxGVMYIvIkbsqWfzdEMnCfqr4VnaiKnyVRY6JEROKBRP+dLkTkDGCuqqZFL7LYJiL1gRqqukxEyqhqerRjKu1E5GrcHOckYDzuLrQVMASXVM/1Rp3HHBuda0wUiMj1wHb8Fj33kup0YJuIDIlSaDFJRCqIyDMisgO3HJ3vLv8+EZkhIm2iGF4suBnYD3RS1WtV9SFVvQS3oEUKcHs0gytOlkSNiTAvQY7CjVTc4HcoHngC1wQ2TkT6Rz662CMiicD3wAO4D/R1ZE8xqgicBXwvIs2iEV+M6ACMVdW1/i+q6jxgItAjKlFFgCVRYyLvQWAzcLKqfuZ7UVVTVfUfQCdgB/BwdMKLOY8AXYC7cHNvP/Ad8Pay/BNuz8tHoxFcjKiCW6oymFVArQjGElGWRI2JvBOBz3wDigJ5r3+G3ypGpkguA6ao6qvevNwcA0FU9T3c3VKfaAQXI8rgRpQHk4abvhWTLIkaE3lp5P/NPNErZ4quAbAonzIrcQtcGFMolkSNibyfgaEi0jLYQRFpgtus+NdIBhXDdpP/2rkneeWMKRRbbMGYyHsWNyH9BxF5EZgLHAQq4wZg3A1UBf4ZrQBjzFfAMBE5T1WnBB4UkYuBAcDIiEcWWzp5q3Ed8zqAiFxLzjWjAbf4SDHHVaxsnqgxUSAi1wCv4JKl/39Cwc21u720f7iUFCJSF7cYen3ga9xqOj1x+7p2wyXQ3UBXVd0arThLMxHJJKCv2f+w9zPwuOCWWizVa0RbEjUmSkSkGjAQt+9ideAwbh3d8aq6L4qhxRxvObo3gfM49m5oNjBMVVdFPLAYISIhL1Gpqk+EM5ZIsyRqjDluiEg93HSXargvLUtUdUM0YzKlmyVRY6JERFrj5i0mEKSvCEBVv4xkTLFIRO4HPlTVbdGOxcQeS6LGRJiI1MStL9orr2LEQH9RSeD112Xitp77APjc9g014WJJ1JgIE5GRuLVGlwHTgAPkMiijtPcXlQQicgVud5z+uLv+o7jFFT4AvrbF/k1RWBI1JsJEZCduEfQeqpoR7XiOFyJSBbgQl1D74tYq3g98imvunR296ExpZYstGBN5lYGplkAjS1UPquoYVR0A1ANuA+YDNwLficiGaMZ3vBGRqiJyR7TjKCpLosZE3lKgbbSDOJ556xP/hJvesgrXB90wqkEdJ0TkLBF5H9gGvBTteIrKViwyJvL+DnwhIhep6hfRDuZ4IiLtgMu9P61xyXMpbmedD6MYWkzzFry4AXfX3xJX75nAt9GMKxwsiRoTeZ1xm0J/KiLrcHdCKUHKqapeHNHIYpCInIDbyeVy3A46guuTfh54X1WXRjG8mCUigltM5GbgfFwftABrgNHAu6q6JWoBhokNLDImwrwpFwVhU1zCwK++DwKfA+8DM9U+/IqFiDQHbsLt09oAlzgP4vYc/UBVr41ieGFnd6LGRF7zaAdwnJkIvAd8qarB7vgBEJEaue3xavImImWBi3F3nWfhxtuk4OZDfwhMAo4Ah6ITYfGxJGpMhKnqxmjHcDxR1SF5HReRc3Af/kOAihEJKvZsA2rgNuaeCnwCjPNf1MK17sYeS6LGRImIlMd98Pj6ivB+lsXtNHK+qoa8sLfJnYg0wA1yuQG39KJvoIsJTU3c7kMvAp+p6uLohhM51idqTISJSEXcwIoLcAk0V9YnGj4iEg8Mxt119sc1OQqwmuyBLrYVWghE5EHgGtzALQV2AmOBj1V1rlcmE3hTVf8StUCLgc0TNSbyHgcuAfYAU3DL0K0AvgE24j7Yd+GSrCkiEWklIs8Am3EDi3wjRecDZ6hqG1X9pyXQ0Knqv1S1A9AdeBVXv3cBc0RkrYg8HdUAi5HdiRoTYSKyCigPtFPVJBH5Ckj1TWcRkcdwifYKVf00iqGWWiKSAFyKu+s8A/fFJA3XX/cBbrBLzN0VlRTeXf/5wHXAINyaxeCmFr2BW2YxJsYGWBI1JsJEJBn4n6re4T0fAdyiqvX9yswDDqrq2dGJsvQSkVeBq3B7hmYC3wMfA5/6Rt/GatNiSSQiVXFzdK8FTiN7s4U5uHm6I6MVWzhYc64xkZdGzqH+a4A6IlLH77XvcCvqmML7C25w1gigoar2UdW3bPpKdKjqAVUdqapn4FYregpYD5yOuyst1SyJGhN5a4GT/Z771m7t6PdaOdydlCm8FUAl4DFgsoj8U0Q6RTckA6Cq61V1hKq2As4E3o52TEVlSdSYyPsC6C8iT4hIDdwSgPuAB0Wkkrfiy2W4b+umkFS1PdADd5fTCLcu7nwRWSEij4mI3eEXIxGJ9xZfCHasn4gkAqjqD6p6a2SjCz9LosZE3gvAPOARYKi3is4LwNm4ZLoGqEsMNHVFi6rOU9U7ccvOXYhbOacZrol3Oa5f7gQRsZ1bwkhEbgXWAQOCHEvErVy0UUTujnRsxcUGFhkTBd7oxUuABaq62lus+37gStyUl/dV9fVoxhhrRKQabkPu64Ce3suZuO3Q3sctEnAw+LtNfrxpRA/gvqDcrqpvBhxvCIwEzsEt9POKqt4T6TjDzZKoMREiIo1xk/xr4ob6T1HVPdGN6vgkIi1xC6Rfg7tDVeCoqlaKZlyllYgMwN1l/gZcqqor8yjbHhgHtALOVdXpkYmyeFgSNSYCRORJ4CFyrlCUDNynqm9FJyoDICJn4BLqxapaPdrxlEYiMhE3UKilqu4uQPkTcPu4TlbVC4s7vuJkSdSYYiYiV+N2EUnC9c1txX0LH4JLqqX+23gsEJGEvHZ5MbkTkV3AN4XZ5kxEvgBOU9W6xRdZ8bMF6I0pfjcD+4HuqrrW96KIdMP1x90OWBKNMkugRVIF10VRGKtxqxqVajY615ji1wEY659AwY0gxe112SMqURkTPttwI6ELozZuofpSzZKoMcWvCm5B+WBWAbUiGIsJQkSqisgd0Y6jFFsMnOtt75cvb23jgbjpXKWaJVFjil8Z3GbFwaThlqgzUSAiZ4nI+7g7qZeiHU8pNgo3t/nfBSz/Iu7L47vFFVCkWJ+oMea4IiJ1cZtx34hby9W3Ife30YyrNFPVr7wRurd6K279A/hR/UauenOjewEPA/1wW9F9FI14w8mSqDEm5nmLWQzEDfLy7ScquObE0bgNuQs7MMbkdC3wGXAebj70YRFZh9tsoTrQFLemsQBzgSGqmhqlWMPGprgYU8y8bbfGe38CXYib6nI97sMlB1Ut9c1d0eTdFd2EmwfaAFfHB3H91B8UZkqGKRgRuQFX56eS8990Gm40+mhV/SAasRUHS6LGFDMvieb2H833IRN4XABV1XhMoXiLn1+Mu+s8Czf2IwX4GrcZ9yTgCLafaLESkcpAQ9xuRHuALaqaHNWgioE15xpT/J6IdgDHmW1ADdxgrqnAJ8A4/3VxXeuuKU6qegi3LV1MsyRqTDFTVUuikVUTtzrUi7hF5RdHN5zYJyJNQn2vqm4KZyyRZs25xpiYIiIP4haWPxHXTL4TGAt8rKpzvTKZWHNu2OTTZZEXVdVSfTNnSdQYE5NEpCtu27MrcKvjKLAB17z7EJZEw0ZEZhI8iTbDjcqdldt7VbVP8UQVGZZEjTExzZufeD4uoQ4CErxDW3Abn3+oqhujFF5ME5HHgcdieYCcrVhkjIlpqpqhql+p6qVAPeBWYA7QCPg7sE5EZovIsGjGaUonuxM1xhyXvDmkvo25W2BTisLO7kSNMSZGqep6VR2hqq1wG0q/He2YTOljSdQYE7NEJN5bfCHYsX4ikgigqj+o6q2Rjc7EAkuixpiYJCK3AuuAAUGOJeJWLtooIndHOjYTOyyJGmNijog8A7yOGzwUbLPoqrhdWxKBF0TkxchFZ2KJDSwyxsQUERmAu8v8DbhUVVfmUbY9MA5oBZyrqtMjE2VsEZEZuRxqRt7zRFVV+xZLUBFiSdQYE1O8fS3PBFqq6u4ClD8BWApMVtULizu+WOStWBSKUj8iulQvt2SMMUH0ACYUJIECqOpqEfkaOK14w4ppzaMdQLRYEjXGxJoquNWICmM1blUjE4LjecUnG1hkjIk12wg+mCgvtXEL1ZsQiMg6Ebkr2nFEgyVRY0ysWQycKyLlC1JYRBKAgcCaYo0qtjXDbb593LEkaoyJNaOAusC/C1j+RaAW8G5xBWRilyVRY0xMUdWvgInArSLytYicJiLiX8ZbyegMEZkCDAPmAx9FIVxTytnAImNMLLoW+Aw4D+gPHBaRdcAhoDpu7mIlQIC5wBBVTY1SrLGimog0KeybVHVTcQQTKTZP1BgTs0TkBuAm4FRcwvRJA2YDo1X1g2jEFku8eaKhJBNV1VJ9M2dJ1BgT80SkMtAQN/hlD7BFVZOjGlQM8ZLoRmBDYd+rqn3CHlAEWRI1xhhTJF4SHaGqT0Y7lkgr1bfRxhgTKJR+OZ/S3j9nIs+SqDEm1mwgxP457DPRFJL9gzHGxJrZBE+izch7RxFjCs36RI0xxwUReRx4rLTvGlISeXX7narOjnYskWZ3osYYY4pEVZ+IdgzRYisWGWOMMSGyJGqMMcaEyJKoMcYYEyJLosYYY0yILIkaY4wxIbLRucaYmCIiM3I51Cyf46qqfYslKBOzbJ6oMSameOu4hkJtDqkpLLsTNcbEmubRDsAcP+xO1BhjjAmRDSwyxsQUEVknIndFOw5zfLAkaoyJNc1wm28bU+wsiRpjjDEhsiRqjDHGhMhG5xpjYlE1EWlS2Dep6qbiCMbELhuda4yJKd480VA+2FRV7cbCFIr9gzHGxKJNwIZoB2FinyVRY0wsekdVn4x2ECb22cAiY4wxJkSWRI0xxpgQWRI1xhhjQmRJ1BgTa54AZkY7CHN8sCkuxhhjTIjsTtQYY4wJkSVRY4wxJkSWRI0xxpgQWRI1xhhjQmRJ1BhjjAnR/wPRA7HPMAbk6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 0\n",
    "methods = [('EMOGI', 'EMOGI'), ('Random Forest', 'Random_Forest'),\n",
    "           ('DeepWalk + SVM', 'DeepWalk'), ('GCN w/o Features', 'GCN_Featureless'),\n",
    "           ('PageRank', 'PageRank'), ('HotNet2\\nDiffusion', 'RWR'),\n",
    "           ('MutSigCV', 'MutSigCV'), ('DeepWalk +\\nFeatures RF', 'RF_dwfeat'), ('20/20+', '2020plus')]\n",
    "\n",
    "metrics_all = pd.DataFrame(index=[m[0] for m in methods])\n",
    "random_metrics = []\n",
    "data = postprocessing.get_training_data(avg_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "\n",
    "# remove overlaps and construct y_true\n",
    "test_genes_pos = avg_test_genes_nooverlap\n",
    "test_genes_neg = avg_test_neg_nooverlap\n",
    "test_genes = pd.concat((test_genes_pos, test_genes_neg))\n",
    "y_true = np.concatenate((np.ones(len(test_genes_pos)), np.zeros(len(test_genes_neg))), axis=0)\n",
    "\n",
    "# compute performance for all methods\n",
    "for model_dir in [pancan_model_path, avg_model_path, patient_model_path]:\n",
    "    metric_values = []\n",
    "    pred_all, _ = postprocessing.compute_predictions_competitors(model_dir,\n",
    "                                                                         network_name='CPDB',\n",
    "                                                                         plot_correlations=False\n",
    "                                                                        )\n",
    "    pred_test = pred_all[pred_all.index.isin(test_genes.Name)]\n",
    "    pred_test = pred_test.reindex(test_genes.Name)\n",
    "\n",
    "    for name, colname in methods:\n",
    "        aupr = average_precision_score(y_true=y_true, y_score=pred_test[colname])\n",
    "        metric_values.append(aupr)\n",
    "    metrics_all[labels[c]] = metric_values\n",
    "    c += 1\n",
    "\n",
    "random_y = y_true.sum() / y_true.shape[0]\n",
    "metrics_all.loc['Random', :] = [random_y]*3\n",
    "\n",
    "fig = plt.figure(figsize=(5, 10))\n",
    "cmap = sns.color_palette(\"Blues\", n_colors=50)\n",
    "\n",
    "sns.heatmap(metrics_all, cbar=False, cmap=cmap, cbar_kws={'use_gridspec': False, 'orientation': 'vertical'},\n",
    "            annot=True, annot_kws={\"size\": 30, 'weight': 'medium'})\n",
    "_ = plt.gca().set_xticklabels(metrics_all.columns, rotation=90, fontsize=20)\n",
    "_ = plt.gca().set_yticklabels(metrics_all.index, rotation=0, fontsize=20)\n",
    "fig.savefig('../data/cancer_specific/heatmap_all_{}.svg'.format(cancer_type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncg_cancer_gene_path = '../data/pancancer/NCG/cancergenes_list.txt'\n",
    "known_cancer_genes = []\n",
    "candidate_cancer_genes = []\n",
    "n = 0\n",
    "with open(ncg_cancer_gene_path, 'r') as f:\n",
    "    for line in f.readlines():\n",
    "        n += 1\n",
    "        if n == 1:\n",
    "            continue\n",
    "        l = line.strip().split('\\t')\n",
    "        if len(l) == 2:\n",
    "            known_cancer_genes.append(l[0])\n",
    "            candidate_cancer_genes.append(l[1])\n",
    "        else:\n",
    "            candidate_cancer_genes.append(l[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>ENSG00000108091</th>\n",
       "      <td>CCDC6</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.187</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000122861</th>\n",
       "      <td>PLAU</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.260</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000168172</th>\n",
       "      <td>HOOK3</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.056</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000130844</th>\n",
       "      <td>ZNF331</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.048</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000173674</th>\n",
       "      <td>EIF1AX</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000266412</th>\n",
       "      <td>NCOA4</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000125618</th>\n",
       "      <td>PAX8</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000145623</th>\n",
       "      <td>OSMR</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000150630</th>\n",
       "      <td>VEGFC</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  label  Num_Pos  Prob_pos  Std_Pred  \\\n",
       "ID                                                              \n",
       "ENSG00000148400    NOTCH1   True        5     0.999     0.001   \n",
       "ENSG00000133703      KRAS   True        5     0.999     0.001   \n",
       "ENSG00000143549      TPM3   True        5     0.998     0.002   \n",
       "ENSG00000077549     CAPZB   True        5     0.998     0.002   \n",
       "ENSG00000171552    BCL2L1   True        5     0.998     0.002   \n",
       "ENSG00000132170     PPARG   True        5     0.997     0.003   \n",
       "ENSG00000150093     ITGB1   True        5     0.997     0.002   \n",
       "ENSG00000198793      MTOR   True        5     0.997     0.002   \n",
       "ENSG00000166333       ILK   True        5     0.997     0.003   \n",
       "ENSG00000131981    LGALS3   True        5     0.997     0.003   \n",
       "ENSG00000137309     HMGA1   True        5     0.997     0.002   \n",
       "ENSG00000145604      SKP2   True        5     0.997     0.003   \n",
       "ENSG00000126777      KTN1   True        5     0.996     0.002   \n",
       "ENSG00000127914     AKAP9   True        5     0.995     0.005   \n",
       "ENSG00000114354       TFG   True        5     0.994     0.004   \n",
       "ENSG00000047410       TPR   True        5     0.994     0.004   \n",
       "ENSG00000078674      PCM1   True        5     0.994     0.006   \n",
       "ENSG00000108946   PRKAR1A   True        5     0.992     0.004   \n",
       "ENSG00000157404       KIT   True        5     0.986     0.013   \n",
       "ENSG00000239672      NME1   True        5     0.981     0.012   \n",
       "ENSG00000132470     ITGB4   True        5     0.977     0.010   \n",
       "ENSG00000002330       BAD   True        5     0.959     0.034   \n",
       "ENSG00000165409      TSHR   True        5     0.958     0.023   \n",
       "ENSG00000073050     XRCC1   True        5     0.892     0.061   \n",
       "ENSG00000111679     PTPN6   True        4     0.839     0.305   \n",
       "ENSG00000100697    DICER1   True        5     0.824     0.098   \n",
       "ENSG00000126215     XRCC3   True        5     0.816     0.145   \n",
       "ENSG00000197323    TRIM33   True        4     0.800     0.424   \n",
       "ENSG00000133895      MEN1   True        4     0.792     0.429   \n",
       "ENSG00000157764      BRAF   True        4     0.791     0.442   \n",
       "ENSG00000149177     PTPRJ   True        4     0.779     0.394   \n",
       "ENSG00000109819  PPARGC1A   True        4     0.775     0.385   \n",
       "ENSG00000115808      STRN   True        4     0.725     0.394   \n",
       "ENSG00000125968       ID1   True        4     0.643     0.194   \n",
       "ENSG00000165731       RET   True        3     0.639     0.443   \n",
       "ENSG00000114251     WNT5A   True        4     0.600     0.322   \n",
       "ENSG00000213281      NRAS   True        4     0.587     0.325   \n",
       "ENSG00000112715     VEGFA   True        2     0.572     0.274   \n",
       "ENSG00000196154    S100A4   True        1     0.195     0.332   \n",
       "ENSG00000108091     CCDC6   True        0     0.138     0.187   \n",
       "ENSG00000122861      PLAU   True        1     0.119     0.260   \n",
       "ENSG00000168172     HOOK3   True        0     0.027     0.056   \n",
       "ENSG00000130844    ZNF331   True        0     0.024     0.048   \n",
       "ENSG00000173674    EIF1AX   True        0     0.008     0.015   \n",
       "ENSG00000266412     NCOA4   True        0     0.000     0.000   \n",
       "ENSG00000125618      PAX8   True        0     0.000     0.000   \n",
       "ENSG00000145623      OSMR   True        0     0.000     0.000   \n",
       "ENSG00000150630     VEGFC   True        0     0.000     0.000   \n",
       "\n",
       "                 NCG_Known_Cancer_Gene  NCG_Candidate_Cancer_Gene  \\\n",
       "ID                                                                  \n",
       "ENSG00000148400                   True                      False   \n",
       "ENSG00000133703                   True                      False   \n",
       "ENSG00000143549                   True                      False   \n",
       "ENSG00000077549                  False                      False   \n",
       "ENSG00000171552                  False                      False   \n",
       "ENSG00000132170                   True                      False   \n",
       "ENSG00000150093                  False                      False   \n",
       "ENSG00000198793                   True                      False   \n",
       "ENSG00000166333                  False                      False   \n",
       "ENSG00000131981                  False                      False   \n",
       "ENSG00000137309                   True                      False   \n",
       "ENSG00000145604                  False                       True   \n",
       "ENSG00000126777                   True                      False   \n",
       "ENSG00000127914                   True                      False   \n",
       "ENSG00000114354                   True                      False   \n",
       "ENSG00000047410                   True                      False   \n",
       "ENSG00000078674                   True                      False   \n",
       "ENSG00000108946                   True                      False   \n",
       "ENSG00000157404                   True                      False   \n",
       "ENSG00000239672                  False                      False   \n",
       "ENSG00000132470                  False                      False   \n",
       "ENSG00000002330                  False                      False   \n",
       "ENSG00000165409                   True                      False   \n",
       "ENSG00000073050                  False                      False   \n",
       "ENSG00000111679                   True                      False   \n",
       "ENSG00000100697                   True                      False   \n",
       "ENSG00000126215                  False                      False   \n",
       "ENSG00000197323                   True                      False   \n",
       "ENSG00000133895                   True                      False   \n",
       "ENSG00000157764                   True                      False   \n",
       "ENSG00000149177                  False                      False   \n",
       "ENSG00000109819                  False                      False   \n",
       "ENSG00000115808                   True                      False   \n",
       "ENSG00000125968                  False                      False   \n",
       "ENSG00000165731                   True                      False   \n",
       "ENSG00000114251                  False                      False   \n",
       "ENSG00000213281                   True                      False   \n",
       "ENSG00000112715                  False                      False   \n",
       "ENSG00000196154                  False                      False   \n",
       "ENSG00000108091                   True                      False   \n",
       "ENSG00000122861                  False                      False   \n",
       "ENSG00000168172                   True                      False   \n",
       "ENSG00000130844                   True                      False   \n",
       "ENSG00000173674                   True                      False   \n",
       "ENSG00000266412                   True                      False   \n",
       "ENSG00000125618                   True                      False   \n",
       "ENSG00000145623                  False                       True   \n",
       "ENSG00000150630                  False                      False   \n",
       "\n",
       "                 OncoKB_Cancer_Gene  Bailey_et_al_Cancer_Gene  ONGene_Oncogene  \n",
       "ID                                                                              \n",
       "ENSG00000148400                True                      True             True  \n",
       "ENSG00000133703                True                      True             True  \n",
       "ENSG00000143549               False                     False            False  \n",
       "ENSG00000077549               False                     False            False  \n",
       "ENSG00000171552                True                     False             True  \n",
       "ENSG00000132170                True                     False             True  \n",
       "ENSG00000150093               False                     False            False  \n",
       "ENSG00000198793                True                      True             True  \n",
       "ENSG00000166333               False                     False             True  \n",
       "ENSG00000131981               False                     False            False  \n",
       "ENSG00000137309               False                     False             True  \n",
       "ENSG00000145604               False                     False             True  \n",
       "ENSG00000126777               False                     False            False  \n",
       "ENSG00000127914               False                     False             True  \n",
       "ENSG00000114354               False                     False             True  \n",
       "ENSG00000047410               False                     False             True  \n",
       "ENSG00000078674               False                     False            False  \n",
       "ENSG00000108946                True                      True            False  \n",
       "ENSG00000157404                True                      True             True  \n",
       "ENSG00000239672               False                     False             True  \n",
       "ENSG00000132470               False                     False            False  \n",
       "ENSG00000002330               False                     False            False  \n",
       "ENSG00000165409                True                     False            False  \n",
       "ENSG00000073050               False                     False            False  \n",
       "ENSG00000111679               False                     False            False  \n",
       "ENSG00000100697                True                      True            False  \n",
       "ENSG00000126215               False                     False            False  \n",
       "ENSG00000197323               False                     False            False  \n",
       "ENSG00000133895                True                      True            False  \n",
       "ENSG00000157764                True                      True             True  \n",
       "ENSG00000149177               False                     False            False  \n",
       "ENSG00000109819               False                     False            False  \n",
       "ENSG00000115808               False                     False            False  \n",
       "ENSG00000125968               False                     False             True  \n",
       "ENSG00000165731                True                      True             True  \n",
       "ENSG00000114251               False                     False             True  \n",
       "ENSG00000213281                True                      True             True  \n",
       "ENSG00000112715                True                     False            False  \n",
       "ENSG00000196154               False                     False             True  \n",
       "ENSG00000108091               False                     False             True  \n",
       "ENSG00000122861               False                     False            False  \n",
       "ENSG00000168172               False                     False            False  \n",
       "ENSG00000130844               False                     False            False  \n",
       "ENSG00000173674                True                      True            False  \n",
       "ENSG00000266412               False                     False             True  \n",
       "ENSG00000125618               False                     False             True  \n",
       "ENSG00000145623               False                     False            False  \n",
       "ENSG00000150630               False                     False            False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_specific_missed = pred_avg_model[pred_avg_model.label]\n",
    "cancer_specific_missed = cancer_specific_missed[cancer_specific_missed.Prob_pos < thr_avg]\n",
    "cancer_specific_missed = cancer_specific_missed[~cancer_specific_missed.Name.isin(preds_pat.Name)]\n",
    "cancer_specific_missed_ncg = cancer_specific_missed[cancer_specific_missed.Name.isin(known_cancer_genes)]\n",
    "#candidates_missed_by_brca_models = brca_missed_ncg[brca_missed_ncg.index.isin(preds_pancan.index)]\n",
    "candidates_missed_by_ctype_models = cancer_specific_missed[cancer_specific_missed.index.isin(preds_pancan.index)]\n",
    "candidates_missed_by_ctype_models.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where is the training data from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CGC           34\n",
       "expression    24\n",
       "mutation       7\n",
       "Name: evidence, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = postprocessing.get_training_data(avg_model_path)\n",
    "comprehensive, ncg_cand, ncg_known = get_cancer_genes_for_net(avg_model_path)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "\n",
    "cgc = pd.read_csv('../data/pancancer/cosmic/cancer_gene_census.csv')\n",
    "nodes = pd.DataFrame(node_names, columns=['ID', 'Name'])\n",
    "positives = nodes[np.logical_or(np.logical_or(y_train, y_test), y_val)]\n",
    "positives[positives.Name.isin(cgc['Gene Symbol'])]\n",
    "\n",
    "positives['evidence'] = 'None'\n",
    "for evidence_type in ['mutation', 'methylation', 'expression']:\n",
    "    fname = '../data/pancancer/digSEE/{0}/{0}_{1}.txt'.format(evidence_type, cancer_type.upper())\n",
    "    evidence = pd.read_csv(fname, sep='\\t')\n",
    "    high_scores = evidence[evidence['EVIDENCE SENTENCE SCORE'] >= 0.8]\n",
    "    positives.loc[positives.Name.isin(high_scores['GENE SYMBOL'].tolist()),'evidence'] = evidence_type\n",
    "positives.loc[positives.Name.isin(cgc['Gene Symbol']), 'evidence'] = 'CGC'\n",
    "\n",
    "positives.evidence.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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